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A2C playing PongNoFrameskip-v4 from https://github.com/sgoodfriend/rl-algo-impls/tree/0760ef7d52b17f30219a27c18ba52c8895025ae3

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+ # Translations
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+ # Django stuff:
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+ # Flask stuff:
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77
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+ .ipynb_checkpoints
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+ # IPython
81
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82
+ ipython_config.py
83
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84
+ # pyenv
85
+ .python-version
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+
87
+ # pipenv
88
+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
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+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
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+ __pypackages__/
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+
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+ # Celery stuff
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+ celerybeat-schedule
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+ celerybeat.pid
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+ # SageMath parsed files
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+ *.sage.py
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+
104
+ # Environments
105
+ .env
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+ .venv
107
+ env/
108
+ venv/
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+ ENV/
110
+ env.bak/
111
+ venv.bak/
112
+
113
+ # Spyder project settings
114
+ .spyderproject
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116
+
117
+ # Rope project settings
118
+ .ropeproject
119
+
120
+ # mkdocs documentation
121
+ /site
122
+
123
+ # mypy
124
+ .mypy_cache/
125
+ .dmypy.json
126
+ dmypy.json
127
+
128
+ # Pyre type checker
129
+ .pyre/
130
+
131
+ # Logging into tensorboard and wandb
132
+ runs/*
133
+ wandb
134
+
135
+ # macOS
136
+ .DS_STORE
137
+
138
+ # Local scratch work
139
+ scratch/*
140
+
141
+ # vscode
142
+ .vscode/
143
+
144
+ # Don't bother tracking saved_models or videos
145
+ saved_models/*
146
+ downloaded_models/*
147
+ videos/*
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ MIT License
2
+
3
+ Copyright (c) 2023 Scott Goodfriend
4
+
5
+ Permission is hereby granted, free of charge, to any person obtaining a copy
6
+ of this software and associated documentation files (the "Software"), to deal
7
+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
11
+
12
+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
15
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
+ SOFTWARE.
README.md ADDED
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1
+ ---
2
+ library_name: rl-algo-impls
3
+ tags:
4
+ - PongNoFrameskip-v4
5
+ - a2c
6
+ - deep-reinforcement-learning
7
+ - reinforcement-learning
8
+ model-index:
9
+ - name: a2c
10
+ results:
11
+ - metrics:
12
+ - type: mean_reward
13
+ value: 21.0 +/- 0.0
14
+ name: mean_reward
15
+ task:
16
+ type: reinforcement-learning
17
+ name: reinforcement-learning
18
+ dataset:
19
+ name: PongNoFrameskip-v4
20
+ type: PongNoFrameskip-v4
21
+ ---
22
+ # **A2C** Agent playing **PongNoFrameskip-v4**
23
+
24
+ This is a trained model of a **A2C** agent playing **PongNoFrameskip-v4** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
25
+
26
+ All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/eyvb72mv.
27
+
28
+ ## Training Results
29
+
30
+ This model was trained from 3 trainings of **A2C** agents using different initial seeds. These agents were trained by checking out [0760ef7](https://github.com/sgoodfriend/rl-algo-impls/tree/0760ef7d52b17f30219a27c18ba52c8895025ae3). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
31
+
32
+ | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
33
+ |:-------|:-------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
34
+ | a2c | PongNoFrameskip-v4 | 1 | 21 | 0 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/0qr8uh8d) |
35
+ | a2c | PongNoFrameskip-v4 | 2 | 21 | 0 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/wgl9ivm1) |
36
+ | a2c | PongNoFrameskip-v4 | 3 | 21 | 0 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/xwzoc2uw) |
37
+
38
+
39
+ ### Prerequisites: Weights & Biases (WandB)
40
+ Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
41
+ By default training goes to a rl-algo-impls project while benchmarks go to
42
+ rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
43
+ models and the model weights are uploaded to WandB.
44
+
45
+ Before doing anything below, you'll need to create a wandb account and run `wandb
46
+ login`.
47
+
48
+
49
+
50
+ ## Usage
51
+ /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
52
+
53
+ Note: While the model state dictionary and hyperaparameters are saved, the latest
54
+ implementation could be sufficiently different to not be able to reproduce similar
55
+ results. You might need to checkout the commit the agent was trained on:
56
+ [0760ef7](https://github.com/sgoodfriend/rl-algo-impls/tree/0760ef7d52b17f30219a27c18ba52c8895025ae3).
57
+ ```
58
+ # Downloads the model, sets hyperparameters, and runs agent for 3 episodes
59
+ python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/xwzoc2uw
60
+ ```
61
+
62
+ Setup hasn't been completely worked out yet, so you might be best served by using Google
63
+ Colab starting from the
64
+ [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
65
+ notebook.
66
+
67
+
68
+
69
+ ## Training
70
+ If you want the highest chance to reproduce these results, you'll want to checkout the
71
+ commit the agent was trained on: [0760ef7](https://github.com/sgoodfriend/rl-algo-impls/tree/0760ef7d52b17f30219a27c18ba52c8895025ae3). While
72
+ training is deterministic, different hardware will give different results.
73
+
74
+ ```
75
+ python train.py --algo a2c --env PongNoFrameskip-v4 --seed 3
76
+ ```
77
+
78
+ Setup hasn't been completely worked out yet, so you might be best served by using Google
79
+ Colab starting from the
80
+ [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
81
+ notebook.
82
+
83
+
84
+
85
+ ## Benchmarking (with Lambda Labs instance)
86
+ This and other models from https://api.wandb.ai/links/sgoodfriend/eyvb72mv were generated by running a script on a Lambda
87
+ Labs instance. In a Lambda Labs instance terminal:
88
+ ```
89
+ git clone git@github.com:sgoodfriend/rl-algo-impls.git
90
+ cd rl-algo-impls
91
+ bash ./lambda_labs/setup.sh
92
+ wandb login
93
+ bash ./lambda_labs/benchmark.sh
94
+ ```
95
+
96
+ ### Alternative: Google Colab Pro+
97
+ As an alternative,
98
+ [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
99
+ can be used. However, this requires a Google Colab Pro+ subscription and running across
100
+ 4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
101
+
102
+
103
+
104
+ ## Hyperparameters
105
+ This isn't exactly the format of hyperparams in hyperparams/a2c.yml, but instead the Wandb Run Config. However, it's very
106
+ close and has some additional data:
107
+ ```
108
+ algo: a2c
109
+ algo_hyperparams:
110
+ ent_coef: 0.01
111
+ vf_coef: 0.25
112
+ env: PongNoFrameskip-v4
113
+ env_hyperparams:
114
+ frame_stack: 4
115
+ n_envs: 16
116
+ no_reward_fire_steps: 500
117
+ no_reward_timeout_steps: 1000
118
+ vec_env_class: async
119
+ n_timesteps: 10000000
120
+ policy_hyperparams:
121
+ activation_fn: relu
122
+ seed: 3
123
+ use_deterministic_algorithms: true
124
+ wandb_entity: null
125
+ wandb_project_name: rl-algo-impls-benchmarks
126
+ wandb_tags:
127
+ - benchmark_0760ef7
128
+ - host_192-9-248-209
129
+
130
+ ```
a2c/a2c.py ADDED
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1
+ import numpy as np
2
+ import torch
3
+ import torch.nn as nn
4
+ import torch.nn.functional as F
5
+
6
+ from dataclasses import asdict, dataclass, field
7
+ from time import perf_counter
8
+ from torch.utils.tensorboard.writer import SummaryWriter
9
+ from typing import List, Optional, Sequence, NamedTuple, TypeVar
10
+
11
+ from shared.algorithm import Algorithm
12
+ from shared.callbacks.callback import Callback
13
+ from shared.gae import compute_advantage, compute_rtg_and_advantage
14
+ from shared.policy.on_policy import ActorCritic
15
+ from shared.schedule import schedule, update_learning_rate
16
+ from shared.stats import log_scalars
17
+ from shared.trajectory import Trajectory, TrajectoryAccumulator
18
+ from wrappers.vectorable_wrapper import (
19
+ VecEnv,
20
+ VecEnvObs,
21
+ single_observation_space,
22
+ single_action_space,
23
+ )
24
+
25
+ A2CSelf = TypeVar("A2CSelf", bound="A2C")
26
+
27
+
28
+ class A2C(Algorithm):
29
+ def __init__(
30
+ self,
31
+ policy: ActorCritic,
32
+ env: VecEnv,
33
+ device: torch.device,
34
+ tb_writer: SummaryWriter,
35
+ learning_rate: float = 7e-4,
36
+ learning_rate_decay: str = "none",
37
+ n_steps: int = 5,
38
+ gamma: float = 0.99,
39
+ gae_lambda: float = 1.0,
40
+ ent_coef: float = 0.0,
41
+ ent_coef_decay: str = "none",
42
+ vf_coef: float = 0.5,
43
+ max_grad_norm: float = 0.5,
44
+ rms_prop_eps: float = 1e-5,
45
+ use_rms_prop: bool = True,
46
+ sde_sample_freq: int = -1,
47
+ normalize_advantage: bool = False,
48
+ ) -> None:
49
+ super().__init__(policy, env, device, tb_writer)
50
+ self.policy = policy
51
+
52
+ self.lr_schedule = schedule(learning_rate_decay, learning_rate)
53
+ if use_rms_prop:
54
+ self.optimizer = torch.optim.RMSprop(
55
+ policy.parameters(), lr=learning_rate, eps=rms_prop_eps
56
+ )
57
+ else:
58
+ self.optimizer = torch.optim.Adam(policy.parameters(), lr=learning_rate)
59
+
60
+ self.n_steps = n_steps
61
+
62
+ self.gamma = gamma
63
+ self.gae_lambda = gae_lambda
64
+
65
+ self.vf_coef = vf_coef
66
+ self.ent_coef_schedule = schedule(ent_coef_decay, ent_coef)
67
+ self.max_grad_norm = max_grad_norm
68
+
69
+ self.sde_sample_freq = sde_sample_freq
70
+ self.normalize_advantage = normalize_advantage
71
+
72
+ def learn(
73
+ self: A2CSelf, total_timesteps: int, callback: Optional[Callback] = None
74
+ ) -> A2CSelf:
75
+ epoch_dim = (self.n_steps, self.env.num_envs)
76
+ step_dim = (self.env.num_envs,)
77
+ obs_space = single_observation_space(self.env)
78
+ act_space = single_action_space(self.env)
79
+
80
+ obs = np.zeros(epoch_dim + obs_space.shape, dtype=obs_space.dtype)
81
+ actions = np.zeros(epoch_dim + act_space.shape, dtype=act_space.dtype)
82
+ rewards = np.zeros(epoch_dim, dtype=np.float32)
83
+ episode_starts = np.zeros(epoch_dim, dtype=np.byte)
84
+ values = np.zeros(epoch_dim, dtype=np.float32)
85
+ logprobs = np.zeros(epoch_dim, dtype=np.float32)
86
+
87
+ next_obs = self.env.reset()
88
+ next_episode_starts = np.ones(step_dim, dtype=np.byte)
89
+
90
+ timesteps_elapsed = 0
91
+ while timesteps_elapsed < total_timesteps:
92
+ start_time = perf_counter()
93
+
94
+ progress = timesteps_elapsed / total_timesteps
95
+ ent_coef = self.ent_coef_schedule(progress)
96
+ learning_rate = self.lr_schedule(progress)
97
+ update_learning_rate(self.optimizer, learning_rate)
98
+ log_scalars(
99
+ self.tb_writer,
100
+ "charts",
101
+ {
102
+ "ent_coef": ent_coef,
103
+ "learning_rate": learning_rate,
104
+ },
105
+ timesteps_elapsed,
106
+ )
107
+
108
+ self.policy.eval()
109
+ self.policy.reset_noise()
110
+ for s in range(self.n_steps):
111
+ timesteps_elapsed += self.env.num_envs
112
+ if self.sde_sample_freq > 0 and s > 0 and s % self.sde_sample_freq == 0:
113
+ self.policy.reset_noise()
114
+
115
+ obs[s] = next_obs
116
+ episode_starts[s] = next_episode_starts
117
+
118
+ actions[s], values[s], logprobs[s], clamped_action = self.policy.step(
119
+ next_obs
120
+ )
121
+ next_obs, rewards[s], next_episode_starts, _ = self.env.step(
122
+ clamped_action
123
+ )
124
+
125
+ advantages = np.zeros(epoch_dim, dtype=np.float32)
126
+ last_gae_lam = 0
127
+ for t in reversed(range(self.n_steps)):
128
+ if t == self.n_steps - 1:
129
+ next_nonterminal = 1.0 - next_episode_starts
130
+ next_value = self.policy.value(next_obs)
131
+ else:
132
+ next_nonterminal = 1.0 - episode_starts[t + 1]
133
+ next_value = values[t + 1]
134
+ delta = (
135
+ rewards[t] + self.gamma * next_value * next_nonterminal - values[t]
136
+ )
137
+ last_gae_lam = (
138
+ delta
139
+ + self.gamma * self.gae_lambda * next_nonterminal * last_gae_lam
140
+ )
141
+ advantages[t] = last_gae_lam
142
+ returns = advantages + values
143
+
144
+ b_obs = torch.tensor(obs.reshape((-1,) + obs_space.shape)).to(self.device)
145
+ b_actions = torch.tensor(actions.reshape((-1,) + act_space.shape)).to(
146
+ self.device
147
+ )
148
+ b_advantages = torch.tensor(advantages.reshape(-1)).to(self.device)
149
+ b_returns = torch.tensor(returns.reshape(-1)).to(self.device)
150
+
151
+ if self.normalize_advantage:
152
+ b_advantages = (b_advantages - b_advantages.mean()) / (
153
+ b_advantages.std() + 1e-8
154
+ )
155
+
156
+ self.policy.train()
157
+ logp_a, entropy, v = self.policy(b_obs, b_actions)
158
+
159
+ pi_loss = -(b_advantages * logp_a).mean()
160
+ value_loss = F.mse_loss(b_returns, v)
161
+ entropy_loss = -entropy.mean()
162
+
163
+ loss = pi_loss + self.vf_coef * value_loss + ent_coef * entropy_loss
164
+
165
+ self.optimizer.zero_grad()
166
+ loss.backward()
167
+ nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
168
+ self.optimizer.step()
169
+
170
+ y_pred = values.reshape(-1)
171
+ y_true = returns.reshape(-1)
172
+ var_y = np.var(y_true).item()
173
+ explained_var = (
174
+ np.nan if var_y == 0 else 1 - np.var(y_true - y_pred).item() / var_y
175
+ )
176
+
177
+ end_time = perf_counter()
178
+ rollout_steps = self.n_steps * self.env.num_envs
179
+ self.tb_writer.add_scalar(
180
+ "train/steps_per_second",
181
+ (rollout_steps) / (end_time - start_time),
182
+ timesteps_elapsed,
183
+ )
184
+
185
+ log_scalars(
186
+ self.tb_writer,
187
+ "losses",
188
+ {
189
+ "loss": loss.item(),
190
+ "pi_loss": pi_loss.item(),
191
+ "v_loss": value_loss.item(),
192
+ "entropy_loss": entropy_loss.item(),
193
+ "explained_var": explained_var,
194
+ },
195
+ timesteps_elapsed,
196
+ )
197
+
198
+ if callback:
199
+ callback.on_step(timesteps_elapsed=rollout_steps)
200
+
201
+ return self
benchmark_publish.py ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import subprocess
3
+ import wandb
4
+ import wandb.apis.public
5
+
6
+ from collections import defaultdict
7
+ from multiprocessing.pool import ThreadPool
8
+ from typing import List, NamedTuple
9
+
10
+
11
+ class RunGroup(NamedTuple):
12
+ algo: str
13
+ env_id: str
14
+
15
+
16
+ if __name__ == "__main__":
17
+ parser = argparse.ArgumentParser()
18
+ parser.add_argument(
19
+ "--wandb-project-name",
20
+ type=str,
21
+ default="rl-algo-impls-benchmarks",
22
+ help="WandB project name to load runs from",
23
+ )
24
+ parser.add_argument(
25
+ "--wandb-entity",
26
+ type=str,
27
+ default=None,
28
+ help="WandB team of project. None uses default entity",
29
+ )
30
+ parser.add_argument("--wandb-tags", type=str, nargs="+", help="WandB tags")
31
+ parser.add_argument("--wandb-report-url", type=str, help="Link to WandB report")
32
+ parser.add_argument(
33
+ "--envs", type=str, nargs="*", help="Optional filter down to these envs"
34
+ )
35
+ parser.add_argument(
36
+ "--exclude-envs",
37
+ type=str,
38
+ nargs="*",
39
+ help="Environments to exclude from publishing",
40
+ )
41
+ parser.add_argument(
42
+ "--huggingface-user",
43
+ type=str,
44
+ default=None,
45
+ help="Huggingface user or team to upload model cards. Defaults to huggingface-cli login user",
46
+ )
47
+ parser.add_argument(
48
+ "--pool-size",
49
+ type=int,
50
+ default=3,
51
+ help="How many publish jobs can run in parallel",
52
+ )
53
+ parser.add_argument(
54
+ "--virtual-display", action="store_true", help="Use headless virtual display"
55
+ )
56
+ # parser.set_defaults(
57
+ # wandb_tags=["benchmark_e47a44c", "host_129-146-2-230"],
58
+ # wandb_report_url="https://api.wandb.ai/links/sgoodfriend/v4wd7cp5",
59
+ # envs=[],
60
+ # exclude_envs=[],
61
+ # )
62
+ args = parser.parse_args()
63
+ print(args)
64
+
65
+ api = wandb.Api()
66
+ all_runs = api.runs(
67
+ f"{args.wandb_entity or api.default_entity}/{args.wandb_project_name}"
68
+ )
69
+
70
+ required_tags = set(args.wandb_tags)
71
+ runs: List[wandb.apis.public.Run] = [
72
+ r
73
+ for r in all_runs
74
+ if required_tags.issubset(set(r.config.get("wandb_tags", [])))
75
+ ]
76
+
77
+ runs_paths_by_group = defaultdict(list)
78
+ for r in runs:
79
+ if r.state != "finished":
80
+ continue
81
+ algo = r.config["algo"]
82
+ env = r.config["env"]
83
+ if args.envs and env not in args.envs:
84
+ continue
85
+ if args.exclude_envs and env in args.exclude_envs:
86
+ continue
87
+ run_group = RunGroup(algo, env)
88
+ runs_paths_by_group[run_group].append("/".join(r.path))
89
+
90
+ def run(run_paths: List[str]) -> None:
91
+ publish_args = ["python", "huggingface_publish.py"]
92
+ publish_args.append("--wandb-run-paths")
93
+ publish_args.extend(run_paths)
94
+ publish_args.append("--wandb-report-url")
95
+ publish_args.append(args.wandb_report_url)
96
+ if args.huggingface_user:
97
+ publish_args.append("--huggingface-user")
98
+ publish_args.append(args.huggingface_user)
99
+ if args.virtual_display:
100
+ publish_args.append("--virtual-display")
101
+ subprocess.run(publish_args)
102
+
103
+ tp = ThreadPool(args.pool_size)
104
+ for run_paths in runs_paths_by_group.values():
105
+ tp.apply_async(run, (run_paths,))
106
+ tp.close()
107
+ tp.join()
benchmarks/benchmark_test.sh ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+
3
+ export WANDB_PROJECT_NAME="rl-algo-impls"
4
+
5
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
6
+
7
+ ALGOS=(
8
+ # "vpg"
9
+ "dqn"
10
+ # "ppo"
11
+ )
12
+ ENVS=(
13
+ # Basic
14
+ "CartPole-v1"
15
+ "MountainCar-v0"
16
+ # "MountainCarContinuous-v0"
17
+ "Acrobot-v1"
18
+ "LunarLander-v2"
19
+ # # PyBullet
20
+ # "HalfCheetahBulletEnv-v0"
21
+ # "AntBulletEnv-v0"
22
+ # "HopperBulletEnv-v0"
23
+ # "Walker2DBulletEnv-v0"
24
+ # # CarRacing
25
+ # "CarRacing-v0"
26
+ # Atari
27
+ "PongNoFrameskip-v4"
28
+ "BreakoutNoFrameskip-v4"
29
+ "SpaceInvadersNoFrameskip-v4"
30
+ "QbertNoFrameskip-v4"
31
+ )
32
+ train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
benchmarks/colab_atari1.sh ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+ ALGOS="ppo"
3
+ ENVS="PongNoFrameskip-v4 BreakoutNoFrameskip-v4"
4
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
5
+ train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
benchmarks/colab_atari2.sh ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+ ALGOS="ppo"
3
+ ENVS="SpaceInvadersNoFrameskip-v4 QbertNoFrameskip-v4"
4
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
5
+ train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
benchmarks/colab_basic.sh ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+ ALGOS="ppo"
3
+ ENVS="CartPole-v1 MountainCar-v0 MountainCarContinuous-v0 Acrobot-v1 LunarLander-v2"
4
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
5
+ train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
benchmarks/colab_benchmark.ipynb ADDED
@@ -0,0 +1,195 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "provenance": [],
7
+ "machine_shape": "hm",
8
+ "authorship_tag": "ABX9TyOGIH7rqgasim3Sz7b1rpoE",
9
+ "include_colab_link": true
10
+ },
11
+ "kernelspec": {
12
+ "name": "python3",
13
+ "display_name": "Python 3"
14
+ },
15
+ "language_info": {
16
+ "name": "python"
17
+ },
18
+ "gpuClass": "standard",
19
+ "accelerator": "GPU"
20
+ },
21
+ "cells": [
22
+ {
23
+ "cell_type": "markdown",
24
+ "metadata": {
25
+ "id": "view-in-github",
26
+ "colab_type": "text"
27
+ },
28
+ "source": [
29
+ "<a href=\"https://colab.research.google.com/github/sgoodfriend/rl-algo-impls/blob/main/benchmarks/colab_benchmark.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
30
+ ]
31
+ },
32
+ {
33
+ "cell_type": "markdown",
34
+ "source": [
35
+ "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
36
+ "## Parameters\n",
37
+ "\n",
38
+ "\n",
39
+ "1. Wandb\n",
40
+ "\n"
41
+ ],
42
+ "metadata": {
43
+ "id": "S-tXDWP8WTLc"
44
+ }
45
+ },
46
+ {
47
+ "cell_type": "code",
48
+ "source": [
49
+ "from getpass import getpass\n",
50
+ "import os\n",
51
+ "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
52
+ ],
53
+ "metadata": {
54
+ "id": "1ZtdYgxWNGwZ"
55
+ },
56
+ "execution_count": null,
57
+ "outputs": []
58
+ },
59
+ {
60
+ "cell_type": "markdown",
61
+ "source": [
62
+ "## Setup\n",
63
+ "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
64
+ ],
65
+ "metadata": {
66
+ "id": "bsG35Io0hmKG"
67
+ }
68
+ },
69
+ {
70
+ "cell_type": "code",
71
+ "source": [
72
+ "%%capture\n",
73
+ "!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
74
+ ],
75
+ "metadata": {
76
+ "id": "k5ynTV25hdAf"
77
+ },
78
+ "execution_count": null,
79
+ "outputs": []
80
+ },
81
+ {
82
+ "cell_type": "markdown",
83
+ "source": [
84
+ "Installing the correct packages:\n",
85
+ "\n",
86
+ "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
87
+ ],
88
+ "metadata": {
89
+ "id": "jKxGok-ElYQ7"
90
+ }
91
+ },
92
+ {
93
+ "cell_type": "code",
94
+ "source": [
95
+ "%%capture\n",
96
+ "!apt install python-opengl\n",
97
+ "!apt install ffmpeg\n",
98
+ "!apt install xvfb\n",
99
+ "!apt install swig"
100
+ ],
101
+ "metadata": {
102
+ "id": "nn6EETTc2Ewf"
103
+ },
104
+ "execution_count": null,
105
+ "outputs": []
106
+ },
107
+ {
108
+ "cell_type": "code",
109
+ "source": [
110
+ "%%capture\n",
111
+ "%cd /content/rl-algo-impls\n",
112
+ "!pip install -r colab_requirements.txt"
113
+ ],
114
+ "metadata": {
115
+ "id": "AfZh9rH3yQii"
116
+ },
117
+ "execution_count": null,
118
+ "outputs": []
119
+ },
120
+ {
121
+ "cell_type": "markdown",
122
+ "source": [
123
+ "## Run Once Per Runtime"
124
+ ],
125
+ "metadata": {
126
+ "id": "4o5HOLjc4wq7"
127
+ }
128
+ },
129
+ {
130
+ "cell_type": "code",
131
+ "source": [
132
+ "import wandb\n",
133
+ "wandb.login()"
134
+ ],
135
+ "metadata": {
136
+ "id": "PCXa5tdS2qFX"
137
+ },
138
+ "execution_count": null,
139
+ "outputs": []
140
+ },
141
+ {
142
+ "cell_type": "markdown",
143
+ "source": [
144
+ "## Restart Session beteween runs"
145
+ ],
146
+ "metadata": {
147
+ "id": "AZBZfSUV43JQ"
148
+ }
149
+ },
150
+ {
151
+ "cell_type": "code",
152
+ "source": [
153
+ "%%capture\n",
154
+ "from pyvirtualdisplay import Display\n",
155
+ "\n",
156
+ "virtual_display = Display(visible=0, size=(1400, 900))\n",
157
+ "virtual_display.start()"
158
+ ],
159
+ "metadata": {
160
+ "id": "VzemeQJP2NO9"
161
+ },
162
+ "execution_count": null,
163
+ "outputs": []
164
+ },
165
+ {
166
+ "cell_type": "markdown",
167
+ "source": [
168
+ "The below 5 bash scripts train agents on environments with 3 seeds each:\n",
169
+ "- colab_basic.sh and colab_pybullet.sh test on a set of basic gym environments and 4 PyBullet environments. Running both together will likely take about 18 hours. This is likely to run into runtime limits for free Colab and Colab Pro, but is fine for Colab Pro+.\n",
170
+ "- colab_carracing.sh only trains 3 seeds on CarRacing-v0, which takes almost 22 hours on Colab Pro+ on high-RAM, standard GPU.\n",
171
+ "- colab_atari1.sh and colab_atari2.sh likely need to be run separately because each takes about 19 hours on high-RAM, standard GPU."
172
+ ],
173
+ "metadata": {
174
+ "id": "nSHfna0hLlO1"
175
+ }
176
+ },
177
+ {
178
+ "cell_type": "code",
179
+ "source": [
180
+ "%cd /content/rl-algo-impls\n",
181
+ "os.environ[\"BENCHMARK_MAX_PROCS\"] = str(1) # Can't reliably raise this to 2+, but would make it faster.\n",
182
+ "!./benchmarks/colab_basic.sh\n",
183
+ "!./benchmarks/colab_pybullet.sh\n",
184
+ "# !./benchmarks/colab_carracing.sh\n",
185
+ "# !./benchmarks/colab_atari1.sh\n",
186
+ "# !./benchmarks/colab_atari2.sh"
187
+ ],
188
+ "metadata": {
189
+ "id": "07aHYFH1zfXa"
190
+ },
191
+ "execution_count": null,
192
+ "outputs": []
193
+ }
194
+ ]
195
+ }
benchmarks/colab_carracing.sh ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+ ALGOS="ppo"
3
+ ENVS="CarRacing-v0"
4
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
5
+ train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
benchmarks/colab_pybullet.sh ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+ ALGOS="ppo"
3
+ ENVS="HalfCheetahBulletEnv-v0 AntBulletEnv-v0 HopperBulletEnv-v0 Walker2DBulletEnv-v0"
4
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
5
+ train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
benchmarks/train_loop.sh ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ train_loop () {
2
+ local WANDB_TAGS="benchmark_$(git rev-parse --short HEAD) host_$(hostname)"
3
+ local algo
4
+ local env
5
+ local seed
6
+ local WANDB_PROJECT_NAME="${WANDB_PROJECT_NAME:-rl-algo-impls-benchmarks}"
7
+ local SEEDS="${SEEDS:-1 2 3}"
8
+ for algo in $(echo $1); do
9
+ for env in $(echo $2); do
10
+ for seed in $SEEDS; do
11
+ echo python train.py --algo $algo --env $env --seed $seed --pool-size 1 --wandb-tags $WANDB_TAGS --wandb-project-name $WANDB_PROJECT_NAME
12
+ done
13
+ done
14
+ done
15
+ }
colab_enjoy.ipynb ADDED
@@ -0,0 +1,198 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "provenance": [],
7
+ "machine_shape": "hm",
8
+ "authorship_tag": "ABX9TyN6S7kyJKrM5x0OOiN+CgTc",
9
+ "include_colab_link": true
10
+ },
11
+ "kernelspec": {
12
+ "name": "python3",
13
+ "display_name": "Python 3"
14
+ },
15
+ "language_info": {
16
+ "name": "python"
17
+ },
18
+ "gpuClass": "standard",
19
+ "accelerator": "GPU"
20
+ },
21
+ "cells": [
22
+ {
23
+ "cell_type": "markdown",
24
+ "metadata": {
25
+ "id": "view-in-github",
26
+ "colab_type": "text"
27
+ },
28
+ "source": [
29
+ "<a href=\"https://colab.research.google.com/github/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
30
+ ]
31
+ },
32
+ {
33
+ "cell_type": "markdown",
34
+ "source": [
35
+ "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
36
+ "## Parameters\n",
37
+ "\n",
38
+ "\n",
39
+ "1. Wandb\n",
40
+ "\n"
41
+ ],
42
+ "metadata": {
43
+ "id": "S-tXDWP8WTLc"
44
+ }
45
+ },
46
+ {
47
+ "cell_type": "code",
48
+ "source": [
49
+ "from getpass import getpass\n",
50
+ "import os\n",
51
+ "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
52
+ ],
53
+ "metadata": {
54
+ "id": "1ZtdYgxWNGwZ"
55
+ },
56
+ "execution_count": null,
57
+ "outputs": []
58
+ },
59
+ {
60
+ "cell_type": "markdown",
61
+ "source": [
62
+ "2. enjoy.py parameters"
63
+ ],
64
+ "metadata": {
65
+ "id": "ao0nAh3MOdN7"
66
+ }
67
+ },
68
+ {
69
+ "cell_type": "code",
70
+ "source": [
71
+ "WANDB_RUN_PATH=\"sgoodfriend/rl-algo-impls-benchmarks/rd0lisee\""
72
+ ],
73
+ "metadata": {
74
+ "id": "jKL_NFhVOjSc"
75
+ },
76
+ "execution_count": 2,
77
+ "outputs": []
78
+ },
79
+ {
80
+ "cell_type": "markdown",
81
+ "source": [
82
+ "## Setup\n",
83
+ "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
84
+ ],
85
+ "metadata": {
86
+ "id": "bsG35Io0hmKG"
87
+ }
88
+ },
89
+ {
90
+ "cell_type": "code",
91
+ "source": [
92
+ "%%capture\n",
93
+ "!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
94
+ ],
95
+ "metadata": {
96
+ "id": "k5ynTV25hdAf"
97
+ },
98
+ "execution_count": 3,
99
+ "outputs": []
100
+ },
101
+ {
102
+ "cell_type": "markdown",
103
+ "source": [
104
+ "Installing the correct packages:\n",
105
+ "\n",
106
+ "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
107
+ ],
108
+ "metadata": {
109
+ "id": "jKxGok-ElYQ7"
110
+ }
111
+ },
112
+ {
113
+ "cell_type": "code",
114
+ "source": [
115
+ "%%capture\n",
116
+ "!apt install python-opengl\n",
117
+ "!apt install ffmpeg\n",
118
+ "!apt install xvfb\n",
119
+ "!apt install swig"
120
+ ],
121
+ "metadata": {
122
+ "id": "nn6EETTc2Ewf"
123
+ },
124
+ "execution_count": 4,
125
+ "outputs": []
126
+ },
127
+ {
128
+ "cell_type": "code",
129
+ "source": [
130
+ "%%capture\n",
131
+ "%cd /content/rl-algo-impls\n",
132
+ "!pip install -r colab_requirements.txt"
133
+ ],
134
+ "metadata": {
135
+ "id": "AfZh9rH3yQii"
136
+ },
137
+ "execution_count": 5,
138
+ "outputs": []
139
+ },
140
+ {
141
+ "cell_type": "markdown",
142
+ "source": [
143
+ "## Run Once Per Runtime"
144
+ ],
145
+ "metadata": {
146
+ "id": "4o5HOLjc4wq7"
147
+ }
148
+ },
149
+ {
150
+ "cell_type": "code",
151
+ "source": [
152
+ "import wandb\n",
153
+ "wandb.login()"
154
+ ],
155
+ "metadata": {
156
+ "id": "PCXa5tdS2qFX"
157
+ },
158
+ "execution_count": null,
159
+ "outputs": []
160
+ },
161
+ {
162
+ "cell_type": "markdown",
163
+ "source": [
164
+ "## Restart Session beteween runs"
165
+ ],
166
+ "metadata": {
167
+ "id": "AZBZfSUV43JQ"
168
+ }
169
+ },
170
+ {
171
+ "cell_type": "code",
172
+ "source": [
173
+ "%%capture\n",
174
+ "from pyvirtualdisplay import Display\n",
175
+ "\n",
176
+ "virtual_display = Display(visible=0, size=(1400, 900))\n",
177
+ "virtual_display.start()"
178
+ ],
179
+ "metadata": {
180
+ "id": "VzemeQJP2NO9"
181
+ },
182
+ "execution_count": 7,
183
+ "outputs": []
184
+ },
185
+ {
186
+ "cell_type": "code",
187
+ "source": [
188
+ "%cd /content/rl-algo-impls\n",
189
+ "!python enjoy.py --wandb-run-path={WANDB_RUN_PATH}"
190
+ ],
191
+ "metadata": {
192
+ "id": "07aHYFH1zfXa"
193
+ },
194
+ "execution_count": null,
195
+ "outputs": []
196
+ }
197
+ ]
198
+ }
colab_requirements.txt ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ AutoROM.accept-rom-license >= 0.4.2, < 0.5
2
+ stable-baselines3[extra] >= 1.7.0, < 1.8
3
+ gym[box2d] >= 0.21.0, < 0.22
4
+ pyglet == 1.5.27
5
+ wandb >= 0.13.10, < 0.14
6
+ pyvirtualdisplay == 3.0
7
+ pybullet >= 3.2.5, < 3.3
8
+ tabulate >= 0.9.0, < 0.10
9
+ huggingface-hub >= 0.12.0, < 0.13
10
+ numexpr >= 2.8.4, < 2.9
11
+ gym3 >= 0.3.3, < 0.4
12
+ glfw >= 1.12.0, < 1.13
13
+ procgen >= 0.10.7, < 0.11
14
+ ipython >= 8.10.0, < 8.11
colab_train.ipynb ADDED
@@ -0,0 +1,200 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "provenance": [],
7
+ "machine_shape": "hm",
8
+ "authorship_tag": "ABX9TyMmemQnx6G7GOnn6XBdjgxY",
9
+ "include_colab_link": true
10
+ },
11
+ "kernelspec": {
12
+ "name": "python3",
13
+ "display_name": "Python 3"
14
+ },
15
+ "language_info": {
16
+ "name": "python"
17
+ },
18
+ "gpuClass": "standard",
19
+ "accelerator": "GPU"
20
+ },
21
+ "cells": [
22
+ {
23
+ "cell_type": "markdown",
24
+ "metadata": {
25
+ "id": "view-in-github",
26
+ "colab_type": "text"
27
+ },
28
+ "source": [
29
+ "<a href=\"https://colab.research.google.com/github/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
30
+ ]
31
+ },
32
+ {
33
+ "cell_type": "markdown",
34
+ "source": [
35
+ "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
36
+ "## Parameters\n",
37
+ "\n",
38
+ "\n",
39
+ "1. Wandb\n",
40
+ "\n"
41
+ ],
42
+ "metadata": {
43
+ "id": "S-tXDWP8WTLc"
44
+ }
45
+ },
46
+ {
47
+ "cell_type": "code",
48
+ "source": [
49
+ "from getpass import getpass\n",
50
+ "import os\n",
51
+ "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
52
+ ],
53
+ "metadata": {
54
+ "id": "1ZtdYgxWNGwZ"
55
+ },
56
+ "execution_count": null,
57
+ "outputs": []
58
+ },
59
+ {
60
+ "cell_type": "markdown",
61
+ "source": [
62
+ "2. train run parameters"
63
+ ],
64
+ "metadata": {
65
+ "id": "ao0nAh3MOdN7"
66
+ }
67
+ },
68
+ {
69
+ "cell_type": "code",
70
+ "source": [
71
+ "ALGO = \"ppo\"\n",
72
+ "ENV = \"CartPole-v1\"\n",
73
+ "SEED = 1"
74
+ ],
75
+ "metadata": {
76
+ "id": "jKL_NFhVOjSc"
77
+ },
78
+ "execution_count": null,
79
+ "outputs": []
80
+ },
81
+ {
82
+ "cell_type": "markdown",
83
+ "source": [
84
+ "## Setup\n",
85
+ "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
86
+ ],
87
+ "metadata": {
88
+ "id": "bsG35Io0hmKG"
89
+ }
90
+ },
91
+ {
92
+ "cell_type": "code",
93
+ "source": [
94
+ "%%capture\n",
95
+ "!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
96
+ ],
97
+ "metadata": {
98
+ "id": "k5ynTV25hdAf"
99
+ },
100
+ "execution_count": null,
101
+ "outputs": []
102
+ },
103
+ {
104
+ "cell_type": "markdown",
105
+ "source": [
106
+ "Installing the correct packages:\n",
107
+ "\n",
108
+ "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
109
+ ],
110
+ "metadata": {
111
+ "id": "jKxGok-ElYQ7"
112
+ }
113
+ },
114
+ {
115
+ "cell_type": "code",
116
+ "source": [
117
+ "%%capture\n",
118
+ "!apt install python-opengl\n",
119
+ "!apt install ffmpeg\n",
120
+ "!apt install xvfb\n",
121
+ "!apt install swig"
122
+ ],
123
+ "metadata": {
124
+ "id": "nn6EETTc2Ewf"
125
+ },
126
+ "execution_count": null,
127
+ "outputs": []
128
+ },
129
+ {
130
+ "cell_type": "code",
131
+ "source": [
132
+ "%%capture\n",
133
+ "%cd /content/rl-algo-impls\n",
134
+ "!pip install -r colab_requirements.txt"
135
+ ],
136
+ "metadata": {
137
+ "id": "AfZh9rH3yQii"
138
+ },
139
+ "execution_count": null,
140
+ "outputs": []
141
+ },
142
+ {
143
+ "cell_type": "markdown",
144
+ "source": [
145
+ "## Run Once Per Runtime"
146
+ ],
147
+ "metadata": {
148
+ "id": "4o5HOLjc4wq7"
149
+ }
150
+ },
151
+ {
152
+ "cell_type": "code",
153
+ "source": [
154
+ "import wandb\n",
155
+ "wandb.login()"
156
+ ],
157
+ "metadata": {
158
+ "id": "PCXa5tdS2qFX"
159
+ },
160
+ "execution_count": null,
161
+ "outputs": []
162
+ },
163
+ {
164
+ "cell_type": "markdown",
165
+ "source": [
166
+ "## Restart Session beteween runs"
167
+ ],
168
+ "metadata": {
169
+ "id": "AZBZfSUV43JQ"
170
+ }
171
+ },
172
+ {
173
+ "cell_type": "code",
174
+ "source": [
175
+ "%%capture\n",
176
+ "from pyvirtualdisplay import Display\n",
177
+ "\n",
178
+ "virtual_display = Display(visible=0, size=(1400, 900))\n",
179
+ "virtual_display.start()"
180
+ ],
181
+ "metadata": {
182
+ "id": "VzemeQJP2NO9"
183
+ },
184
+ "execution_count": null,
185
+ "outputs": []
186
+ },
187
+ {
188
+ "cell_type": "code",
189
+ "source": [
190
+ "%cd /content/rl-algo-impls\n",
191
+ "!python train.py --algo {ALGO} --env {ENV} --seed {SEED}"
192
+ ],
193
+ "metadata": {
194
+ "id": "07aHYFH1zfXa"
195
+ },
196
+ "execution_count": null,
197
+ "outputs": []
198
+ }
199
+ ]
200
+ }
compare_runs.py ADDED
@@ -0,0 +1,187 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import itertools
3
+ import numpy as np
4
+ import pandas as pd
5
+ import wandb
6
+ import wandb.apis.public
7
+
8
+ from collections import defaultdict
9
+ from dataclasses import dataclass
10
+ from typing import Dict, Iterable, List, TypeVar
11
+
12
+ from benchmark_publish import RunGroup
13
+
14
+
15
+ @dataclass
16
+ class Comparison:
17
+ control_values: List[float]
18
+ experiment_values: List[float]
19
+
20
+ def mean_diff_percentage(self) -> float:
21
+ return self._diff_percentage(
22
+ np.mean(self.control_values).item(), np.mean(self.experiment_values).item()
23
+ )
24
+
25
+ def median_diff_percentage(self) -> float:
26
+ return self._diff_percentage(
27
+ np.median(self.control_values).item(),
28
+ np.median(self.experiment_values).item(),
29
+ )
30
+
31
+ def _diff_percentage(self, c: float, e: float) -> float:
32
+ if c == e:
33
+ return 0
34
+ elif c == 0:
35
+ return float("inf") if e > 0 else float("-inf")
36
+ return 100 * (e - c) / c
37
+
38
+ def score(self) -> float:
39
+ return (
40
+ np.sum(
41
+ np.sign((self.mean_diff_percentage(), self.median_diff_percentage()))
42
+ ).item()
43
+ / 2
44
+ )
45
+
46
+
47
+ RunGroupRunsSelf = TypeVar("RunGroupRunsSelf", bound="RunGroupRuns")
48
+
49
+
50
+ class RunGroupRuns:
51
+ def __init__(
52
+ self,
53
+ run_group: RunGroup,
54
+ control: List[str],
55
+ experiment: List[str],
56
+ summary_stats: List[str] = ["best_eval", "eval", "train_rolling"],
57
+ summary_metrics: List[str] = ["mean", "result"],
58
+ ) -> None:
59
+ self.algo = run_group.algo
60
+ self.env = run_group.env_id
61
+ self.control = set(control)
62
+ self.experiment = set(experiment)
63
+
64
+ self.summary_stats = summary_stats
65
+ self.summary_metrics = summary_metrics
66
+
67
+ self.control_runs = []
68
+ self.experiment_runs = []
69
+
70
+ def add_run(self, run: wandb.apis.public.Run) -> None:
71
+ wandb_tags = set(run.config.get("wandb_tags", []))
72
+ if self.control & wandb_tags:
73
+ self.control_runs.append(run)
74
+ elif self.experiment & wandb_tags:
75
+ self.experiment_runs.append(run)
76
+
77
+ def comparisons_by_metric(self) -> Dict[str, Comparison]:
78
+ c_by_m = {}
79
+ for metric in (
80
+ f"{s}/{m}"
81
+ for s, m in itertools.product(self.summary_stats, self.summary_metrics)
82
+ ):
83
+ c_by_m[metric] = Comparison(
84
+ [c.summary[metric] for c in self.control_runs],
85
+ [e.summary[metric] for e in self.experiment_runs],
86
+ )
87
+ return c_by_m
88
+
89
+ @staticmethod
90
+ def data_frame(rows: Iterable[RunGroupRunsSelf]) -> pd.DataFrame:
91
+ results = defaultdict(list)
92
+ for r in rows:
93
+ if not r.control_runs or not r.experiment_runs:
94
+ continue
95
+ results["algo"].append(r.algo)
96
+ results["env"].append(r.env)
97
+ results["control"].append(r.control)
98
+ results["expierment"].append(r.experiment)
99
+ c_by_m = r.comparisons_by_metric()
100
+ results["score"].append(
101
+ sum(m.score() for m in c_by_m.values()) / len(c_by_m)
102
+ )
103
+ for m, c in c_by_m.items():
104
+ results[f"{m}_mean"].append(c.mean_diff_percentage())
105
+ results[f"{m}_median"].append(c.median_diff_percentage())
106
+ return pd.DataFrame(results)
107
+
108
+
109
+ if __name__ == "__main__":
110
+ parser = argparse.ArgumentParser()
111
+ parser.add_argument(
112
+ "-p",
113
+ "--wandb-project-name",
114
+ type=str,
115
+ default="rl-algo-impls-benchmarks",
116
+ help="WandB project name to load runs from",
117
+ )
118
+ parser.add_argument(
119
+ "--wandb-entity",
120
+ type=str,
121
+ default=None,
122
+ help="WandB team. None uses default entity",
123
+ )
124
+ parser.add_argument(
125
+ "-n",
126
+ "--wandb-hostname-tag",
127
+ type=str,
128
+ nargs="*",
129
+ help="WandB tags for hostname (i.e. host_192-9-145-26)",
130
+ )
131
+ parser.add_argument(
132
+ "-c",
133
+ "--wandb-control-tag",
134
+ type=str,
135
+ nargs="+",
136
+ help="WandB tag for control commit (i.e. benchmark_5598ebc)",
137
+ )
138
+ parser.add_argument(
139
+ "-e",
140
+ "--wandb-experiment-tag",
141
+ type=str,
142
+ nargs="+",
143
+ help="WandB tag for experiment commit (i.e. benchmark_5540e1f)",
144
+ )
145
+ parser.add_argument(
146
+ "--exclude-envs",
147
+ type=str,
148
+ nargs="*",
149
+ help="Environments to exclude from comparison",
150
+ )
151
+ # parser.set_defaults(
152
+ # wandb_hostname_tag=["host_150-230-44-105", "host_155-248-214-128"],
153
+ # wandb_control_tag=["benchmark_fbc943f"],
154
+ # wandb_experiment_tag=["benchmark_f59bf74"],
155
+ # exclude_envs=[],
156
+ # )
157
+ args = parser.parse_args()
158
+ print(args)
159
+
160
+ api = wandb.Api()
161
+ all_runs = api.runs(
162
+ path=f"{args.wandb_entity or api.default_entity}/{args.wandb_project_name}",
163
+ order="+created_at",
164
+ )
165
+
166
+ runs_by_run_group: Dict[RunGroup, RunGroupRuns] = {}
167
+ wandb_hostname_tags = set(args.wandb_hostname_tag)
168
+ for r in all_runs:
169
+ if r.state != "finished":
170
+ continue
171
+ wandb_tags = set(r.config.get("wandb_tags", []))
172
+ if not wandb_tags or not wandb_hostname_tags & wandb_tags:
173
+ continue
174
+ rg = RunGroup(r.config["algo"], r.config.get("env_id") or r.config["env"])
175
+ if args.exclude_envs and rg.env_id in args.exclude_envs:
176
+ continue
177
+ if rg not in runs_by_run_group:
178
+ runs_by_run_group[rg] = RunGroupRuns(
179
+ rg,
180
+ args.wandb_control_tag,
181
+ args.wandb_experiment_tag,
182
+ )
183
+ runs_by_run_group[rg].add_run(r)
184
+ df = RunGroupRuns.data_frame(runs_by_run_group.values()).round(decimals=2)
185
+ print(f"**Total Score: {sum(df.score)}**")
186
+ df.loc["mean"] = df.mean(numeric_only=True)
187
+ print(df.to_markdown())
dqn/dqn.py ADDED
@@ -0,0 +1,182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import numpy as np
3
+ import random
4
+ import torch
5
+ import torch.nn as nn
6
+ import torch.nn.functional as F
7
+
8
+ from collections import deque
9
+ from torch.optim import Adam
10
+ from torch.utils.tensorboard.writer import SummaryWriter
11
+ from typing import List, NamedTuple, Optional, TypeVar
12
+
13
+ from dqn.policy import DQNPolicy
14
+ from shared.algorithm import Algorithm
15
+ from shared.callbacks.callback import Callback
16
+ from shared.schedule import linear_schedule
17
+ from wrappers.vectorable_wrapper import VecEnv, VecEnvObs
18
+
19
+
20
+ class Transition(NamedTuple):
21
+ obs: np.ndarray
22
+ action: np.ndarray
23
+ reward: float
24
+ done: bool
25
+ next_obs: np.ndarray
26
+
27
+
28
+ class Batch(NamedTuple):
29
+ obs: np.ndarray
30
+ actions: np.ndarray
31
+ rewards: np.ndarray
32
+ dones: np.ndarray
33
+ next_obs: np.ndarray
34
+
35
+
36
+ class ReplayBuffer:
37
+ def __init__(self, num_envs: int, maxlen: int) -> None:
38
+ self.num_envs = num_envs
39
+ self.buffer = deque(maxlen=maxlen)
40
+
41
+ def add(
42
+ self,
43
+ obs: VecEnvObs,
44
+ action: np.ndarray,
45
+ reward: np.ndarray,
46
+ done: np.ndarray,
47
+ next_obs: VecEnvObs,
48
+ ) -> None:
49
+ assert isinstance(obs, np.ndarray)
50
+ assert isinstance(next_obs, np.ndarray)
51
+ for i in range(self.num_envs):
52
+ self.buffer.append(
53
+ Transition(obs[i], action[i], reward[i], done[i], next_obs[i])
54
+ )
55
+
56
+ def sample(self, batch_size: int) -> Batch:
57
+ ts = random.sample(self.buffer, batch_size)
58
+ return Batch(
59
+ obs=np.array([t.obs for t in ts]),
60
+ actions=np.array([t.action for t in ts]),
61
+ rewards=np.array([t.reward for t in ts]),
62
+ dones=np.array([t.done for t in ts]),
63
+ next_obs=np.array([t.next_obs for t in ts]),
64
+ )
65
+
66
+ def __len__(self) -> int:
67
+ return len(self.buffer)
68
+
69
+
70
+ DQNSelf = TypeVar("DQNSelf", bound="DQN")
71
+
72
+
73
+ class DQN(Algorithm):
74
+ def __init__(
75
+ self,
76
+ policy: DQNPolicy,
77
+ env: VecEnv,
78
+ device: torch.device,
79
+ tb_writer: SummaryWriter,
80
+ learning_rate: float = 1e-4,
81
+ buffer_size: int = 1_000_000,
82
+ learning_starts: int = 50_000,
83
+ batch_size: int = 32,
84
+ tau: float = 1.0,
85
+ gamma: float = 0.99,
86
+ train_freq: int = 4,
87
+ gradient_steps: int = 1,
88
+ target_update_interval: int = 10_000,
89
+ exploration_fraction: float = 0.1,
90
+ exploration_initial_eps: float = 1.0,
91
+ exploration_final_eps: float = 0.05,
92
+ max_grad_norm: float = 10.0,
93
+ ) -> None:
94
+ super().__init__(policy, env, device, tb_writer)
95
+ self.policy = policy
96
+
97
+ self.optimizer = Adam(self.policy.q_net.parameters(), lr=learning_rate)
98
+
99
+ self.target_q_net = copy.deepcopy(self.policy.q_net).to(self.device)
100
+ self.target_q_net.train(False)
101
+ self.tau = tau
102
+ self.target_update_interval = target_update_interval
103
+
104
+ self.replay_buffer = ReplayBuffer(self.env.num_envs, buffer_size)
105
+ self.batch_size = batch_size
106
+
107
+ self.learning_starts = learning_starts
108
+ self.train_freq = train_freq
109
+ self.gradient_steps = gradient_steps
110
+
111
+ self.gamma = gamma
112
+ self.exploration_eps_schedule = linear_schedule(
113
+ exploration_initial_eps,
114
+ exploration_final_eps,
115
+ end_fraction=exploration_fraction,
116
+ )
117
+
118
+ self.max_grad_norm = max_grad_norm
119
+
120
+ def learn(
121
+ self: DQNSelf, total_timesteps: int, callback: Optional[Callback] = None
122
+ ) -> DQNSelf:
123
+ self.policy.train(True)
124
+ obs = self.env.reset()
125
+ obs = self._collect_rollout(self.learning_starts, obs, 1)
126
+ learning_steps = total_timesteps - self.learning_starts
127
+ timesteps_elapsed = 0
128
+ steps_since_target_update = 0
129
+ while timesteps_elapsed < learning_steps:
130
+ progress = timesteps_elapsed / learning_steps
131
+ eps = self.exploration_eps_schedule(progress)
132
+ obs = self._collect_rollout(self.train_freq, obs, eps)
133
+ rollout_steps = self.train_freq
134
+ timesteps_elapsed += rollout_steps
135
+ for _ in range(
136
+ self.gradient_steps if self.gradient_steps > 0 else self.train_freq
137
+ ):
138
+ self.train()
139
+ steps_since_target_update += rollout_steps
140
+ if steps_since_target_update >= self.target_update_interval:
141
+ self._update_target()
142
+ steps_since_target_update = 0
143
+ if callback:
144
+ callback.on_step(timesteps_elapsed=rollout_steps)
145
+ return self
146
+
147
+ def train(self) -> None:
148
+ if len(self.replay_buffer) < self.batch_size:
149
+ return
150
+ o, a, r, d, next_o = self.replay_buffer.sample(self.batch_size)
151
+ o = torch.as_tensor(o, device=self.device)
152
+ a = torch.as_tensor(a, device=self.device).unsqueeze(1)
153
+ r = torch.as_tensor(r, dtype=torch.float32, device=self.device)
154
+ d = torch.as_tensor(d, dtype=torch.long, device=self.device)
155
+ next_o = torch.as_tensor(next_o, device=self.device)
156
+
157
+ with torch.no_grad():
158
+ target = r + (1 - d) * self.gamma * self.target_q_net(next_o).max(1).values
159
+ current = self.policy.q_net(o).gather(dim=1, index=a).squeeze(1)
160
+ loss = F.smooth_l1_loss(current, target)
161
+
162
+ self.optimizer.zero_grad()
163
+ loss.backward()
164
+ if self.max_grad_norm:
165
+ nn.utils.clip_grad_norm_(self.policy.q_net.parameters(), self.max_grad_norm)
166
+ self.optimizer.step()
167
+
168
+ def _collect_rollout(self, timesteps: int, obs: VecEnvObs, eps: float) -> VecEnvObs:
169
+ for _ in range(0, timesteps, self.env.num_envs):
170
+ action = self.policy.act(obs, eps, deterministic=False)
171
+ next_obs, reward, done, _ = self.env.step(action)
172
+ self.replay_buffer.add(obs, action, reward, done, next_obs)
173
+ obs = next_obs
174
+ return obs
175
+
176
+ def _update_target(self) -> None:
177
+ for target_param, param in zip(
178
+ self.target_q_net.parameters(), self.policy.q_net.parameters()
179
+ ):
180
+ target_param.data.copy_(
181
+ self.tau * param.data + (1 - self.tau) * target_param.data
182
+ )
dqn/policy.py ADDED
@@ -0,0 +1,52 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import os
3
+ import torch
4
+
5
+ from typing import Optional, Sequence, TypeVar
6
+
7
+ from dqn.q_net import QNetwork
8
+ from shared.policy.policy import Policy
9
+ from wrappers.vectorable_wrapper import (
10
+ VecEnv,
11
+ VecEnvObs,
12
+ single_observation_space,
13
+ single_action_space,
14
+ )
15
+
16
+ DQNPolicySelf = TypeVar("DQNPolicySelf", bound="DQNPolicy")
17
+
18
+
19
+ class DQNPolicy(Policy):
20
+ def __init__(
21
+ self,
22
+ env: VecEnv,
23
+ hidden_sizes: Sequence[int] = [],
24
+ cnn_feature_dim: int = 512,
25
+ cnn_style: str = "nature",
26
+ cnn_layers_init_orthogonal: Optional[bool] = None,
27
+ impala_channels: Sequence[int] = (16, 32, 32),
28
+ **kwargs,
29
+ ) -> None:
30
+ super().__init__(env, **kwargs)
31
+ self.q_net = QNetwork(
32
+ single_observation_space(env),
33
+ single_action_space(env),
34
+ hidden_sizes,
35
+ cnn_feature_dim=cnn_feature_dim,
36
+ cnn_style=cnn_style,
37
+ cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
38
+ impala_channels=impala_channels,
39
+ )
40
+
41
+ def act(
42
+ self, obs: VecEnvObs, eps: float = 0, deterministic: bool = True
43
+ ) -> np.ndarray:
44
+ assert eps == 0 if deterministic else eps >= 0
45
+ if not deterministic and np.random.random() < eps:
46
+ return np.array(
47
+ [self.env.action_space.sample() for _ in range(self.env.num_envs)]
48
+ )
49
+ else:
50
+ o = self._as_tensor(obs)
51
+ with torch.no_grad():
52
+ return self.q_net(o).argmax(axis=1).cpu().numpy()
dqn/q_net.py ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gym
2
+ import torch as th
3
+ import torch.nn as nn
4
+
5
+ from gym.spaces import Discrete
6
+ from typing import Optional, Sequence, Type
7
+
8
+ from shared.module.feature_extractor import FeatureExtractor
9
+ from shared.module.module import mlp
10
+
11
+
12
+ class QNetwork(nn.Module):
13
+ def __init__(
14
+ self,
15
+ observation_space: gym.Space,
16
+ action_space: gym.Space,
17
+ hidden_sizes: Sequence[int] = [],
18
+ activation: Type[nn.Module] = nn.ReLU, # Used by stable-baselines3
19
+ cnn_feature_dim: int = 512,
20
+ cnn_style: str = "nature",
21
+ cnn_layers_init_orthogonal: Optional[bool] = None,
22
+ impala_channels: Sequence[int] = (16, 32, 32),
23
+ ) -> None:
24
+ super().__init__()
25
+ assert isinstance(action_space, Discrete)
26
+ self._feature_extractor = FeatureExtractor(
27
+ observation_space,
28
+ activation,
29
+ cnn_feature_dim=cnn_feature_dim,
30
+ cnn_style=cnn_style,
31
+ cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
32
+ impala_channels=impala_channels,
33
+ )
34
+ layer_sizes = (
35
+ (self._feature_extractor.out_dim,) + tuple(hidden_sizes) + (action_space.n,)
36
+ )
37
+ self._fc = mlp(layer_sizes, activation)
38
+
39
+ def forward(self, obs: th.Tensor) -> th.Tensor:
40
+ x = self._feature_extractor(obs)
41
+ return self._fc(x)
enjoy.py ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Support for PyTorch mps mode (https://pytorch.org/docs/stable/notes/mps.html)
2
+ import os
3
+
4
+ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
5
+
6
+ from runner.evaluate import EvalArgs, evaluate_model
7
+ from runner.running_utils import base_parser
8
+
9
+
10
+ if __name__ == "__main__":
11
+ parser = base_parser(multiple=False)
12
+ parser.add_argument("--render", default=True, type=bool)
13
+ parser.add_argument("--best", default=True, type=bool)
14
+ parser.add_argument("--n_envs", default=1, type=int)
15
+ parser.add_argument("--n_episodes", default=3, type=int)
16
+ parser.add_argument("--deterministic-eval", default=None, type=bool)
17
+ parser.add_argument(
18
+ "--no-print-returns", action="store_true", help="Limit printing"
19
+ )
20
+ # wandb-run-path overrides base RunArgs
21
+ parser.add_argument("--wandb-run-path", default=None, type=str)
22
+ parser.set_defaults(
23
+ algo=["ppo"],
24
+ )
25
+ args = parser.parse_args()
26
+ args.algo = args.algo[0]
27
+ args.env = args.env[0]
28
+ args = EvalArgs(**vars(args))
29
+
30
+ evaluate_model(args, os.path.dirname(__file__))
environment.yml ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: rl_algo_impls
2
+ channels:
3
+ - pytorch
4
+ - conda-forge
5
+ - nodefaults
6
+ dependencies:
7
+ - python=3.10.*
8
+ - mamba
9
+ - pip
10
+ - poetry
11
+ - pytorch
12
+ - torchvision
13
+ - torchaudio
14
+ - cmake
15
+ - swig
16
+ - ipywidgets
17
+ - black
huggingface_publish.py ADDED
@@ -0,0 +1,189 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
4
+
5
+ import argparse
6
+ import requests
7
+ import shutil
8
+ import subprocess
9
+ import tempfile
10
+ import wandb
11
+ import wandb.apis.public
12
+
13
+ from typing import List, Optional
14
+
15
+ from huggingface_hub.hf_api import HfApi, upload_folder
16
+ from huggingface_hub.repocard import metadata_save
17
+ from pyvirtualdisplay.display import Display
18
+
19
+ from publish.markdown_format import EvalTableData, model_card_text
20
+ from runner.config import EnvHyperparams
21
+ from runner.evaluate import EvalArgs, evaluate_model
22
+ from runner.env import make_eval_env
23
+ from shared.callbacks.eval_callback import evaluate
24
+ from wrappers.vec_episode_recorder import VecEpisodeRecorder
25
+
26
+
27
+ def publish(
28
+ wandb_run_paths: List[str],
29
+ wandb_report_url: str,
30
+ huggingface_user: Optional[str] = None,
31
+ huggingface_token: Optional[str] = None,
32
+ virtual_display: bool = False,
33
+ ) -> None:
34
+ if virtual_display:
35
+ display = Display(visible=False, size=(1400, 900))
36
+ display.start()
37
+
38
+ api = wandb.Api()
39
+ runs = [api.run(rp) for rp in wandb_run_paths]
40
+ algo = runs[0].config["algo"]
41
+ hyperparam_id = runs[0].config["env"]
42
+ evaluations = [
43
+ evaluate_model(
44
+ EvalArgs(
45
+ algo,
46
+ hyperparam_id,
47
+ seed=r.config.get("seed", None),
48
+ render=False,
49
+ best=True,
50
+ n_envs=None,
51
+ n_episodes=10,
52
+ no_print_returns=True,
53
+ wandb_run_path="/".join(r.path),
54
+ ),
55
+ os.path.dirname(__file__),
56
+ )
57
+ for r in runs
58
+ ]
59
+ run_metadata = requests.get(runs[0].file("wandb-metadata.json").url).json()
60
+ table_data = list(EvalTableData(r, e) for r, e in zip(runs, evaluations))
61
+ best_eval = sorted(
62
+ table_data, key=lambda d: d.evaluation.stats.score, reverse=True
63
+ )[0]
64
+
65
+ with tempfile.TemporaryDirectory() as tmpdirname:
66
+ _, (policy, stats, config) = best_eval
67
+
68
+ repo_name = config.model_name(include_seed=False)
69
+ repo_dir_path = os.path.join(tmpdirname, repo_name)
70
+ # Locally clone this repo to a temp directory
71
+ subprocess.run(["git", "clone", ".", repo_dir_path])
72
+ shutil.rmtree(os.path.join(repo_dir_path, ".git"))
73
+ model_path = config.model_dir_path(best=True, downloaded=True)
74
+ shutil.copytree(
75
+ model_path,
76
+ os.path.join(
77
+ repo_dir_path, "saved_models", config.model_dir_name(best=True)
78
+ ),
79
+ )
80
+
81
+ github_url = "https://github.com/sgoodfriend/rl-algo-impls"
82
+ commit_hash = run_metadata.get("git", {}).get("commit", None)
83
+ env_id = runs[0].config.get("env_id") or runs[0].config["env"]
84
+ card_text = model_card_text(
85
+ algo,
86
+ env_id,
87
+ github_url,
88
+ commit_hash,
89
+ wandb_report_url,
90
+ table_data,
91
+ best_eval,
92
+ )
93
+ readme_filepath = os.path.join(repo_dir_path, "README.md")
94
+ os.remove(readme_filepath)
95
+ with open(readme_filepath, "w") as f:
96
+ f.write(card_text)
97
+
98
+ metadata = {
99
+ "library_name": "rl-algo-impls",
100
+ "tags": [
101
+ env_id,
102
+ algo,
103
+ "deep-reinforcement-learning",
104
+ "reinforcement-learning",
105
+ ],
106
+ "model-index": [
107
+ {
108
+ "name": algo,
109
+ "results": [
110
+ {
111
+ "metrics": [
112
+ {
113
+ "type": "mean_reward",
114
+ "value": str(stats.score),
115
+ "name": "mean_reward",
116
+ }
117
+ ],
118
+ "task": {
119
+ "type": "reinforcement-learning",
120
+ "name": "reinforcement-learning",
121
+ },
122
+ "dataset": {
123
+ "name": env_id,
124
+ "type": env_id,
125
+ },
126
+ }
127
+ ],
128
+ }
129
+ ],
130
+ }
131
+ metadata_save(readme_filepath, metadata)
132
+
133
+ video_env = VecEpisodeRecorder(
134
+ make_eval_env(
135
+ config,
136
+ EnvHyperparams(**config.env_hyperparams),
137
+ override_n_envs=1,
138
+ normalize_load_path=model_path,
139
+ ),
140
+ os.path.join(repo_dir_path, "replay"),
141
+ max_video_length=3600,
142
+ )
143
+ evaluate(
144
+ video_env,
145
+ policy,
146
+ 1,
147
+ deterministic=config.eval_params.get("deterministic", True),
148
+ )
149
+
150
+ api = HfApi()
151
+ huggingface_user = huggingface_user or api.whoami()["name"]
152
+ huggingface_repo = f"{huggingface_user}/{repo_name}"
153
+ api.create_repo(
154
+ token=huggingface_token,
155
+ repo_id=huggingface_repo,
156
+ private=False,
157
+ exist_ok=True,
158
+ )
159
+ repo_url = upload_folder(
160
+ repo_id=huggingface_repo,
161
+ folder_path=repo_dir_path,
162
+ path_in_repo="",
163
+ commit_message=f"{algo.upper()} playing {env_id} from {github_url}/tree/{commit_hash}",
164
+ token=huggingface_token,
165
+ )
166
+ print(f"Pushed model to the hub: {repo_url}")
167
+
168
+
169
+ if __name__ == "__main__":
170
+ parser = argparse.ArgumentParser()
171
+ parser.add_argument(
172
+ "--wandb-run-paths",
173
+ type=str,
174
+ nargs="+",
175
+ help="Run paths of the form entity/project/run_id",
176
+ )
177
+ parser.add_argument("--wandb-report-url", type=str, help="Link to WandB report")
178
+ parser.add_argument(
179
+ "--huggingface-user",
180
+ type=str,
181
+ help="Huggingface user or team to upload model cards",
182
+ default=None,
183
+ )
184
+ parser.add_argument(
185
+ "--virtual-display", action="store_true", help="Use headless virtual display"
186
+ )
187
+ args = parser.parse_args()
188
+ print(args)
189
+ publish(**vars(args))
hyperparams/a2c.yml ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ CartPole-v1: &cartpole-defaults
2
+ n_timesteps: !!float 5e5
3
+ env_hyperparams:
4
+ n_envs: 8
5
+
6
+ CartPole-v0:
7
+ <<: *cartpole-defaults
8
+
9
+ MountainCar-v0:
10
+ n_timesteps: !!float 1e6
11
+ env_hyperparams:
12
+ n_envs: 16
13
+ normalize: true
14
+
15
+ MountainCarContinuous-v0:
16
+ n_timesteps: !!float 1e5
17
+ env_hyperparams:
18
+ n_envs: 4
19
+ normalize: true
20
+ # policy_hyperparams:
21
+ # use_sde: true
22
+ # log_std_init: 0.0
23
+ # init_layers_orthogonal: false
24
+ algo_hyperparams:
25
+ n_steps: 100
26
+ sde_sample_freq: 16
27
+
28
+ Acrobot-v1:
29
+ n_timesteps: !!float 5e5
30
+ env_hyperparams:
31
+ normalize: true
32
+ n_envs: 16
33
+
34
+ LunarLander-v2:
35
+ n_timesteps: !!float 1e6
36
+ env_hyperparams:
37
+ n_envs: 8
38
+ normalize: true
39
+ algo_hyperparams:
40
+ n_steps: 5
41
+ gamma: 0.995
42
+ learning_rate: !!float 8.3e-4
43
+ learning_rate_decay: linear
44
+ ent_coef: !!float 1e-5
45
+
46
+ BipedalWalker-v3:
47
+ n_timesteps: !!float 5e6
48
+ env_hyperparams:
49
+ n_envs: 16
50
+ normalize: true
51
+ policy_hyperparams:
52
+ use_sde: true
53
+ log_std_init: -2
54
+ init_layers_orthogonal: false
55
+ algo_hyperparams:
56
+ ent_coef: 0
57
+ max_grad_norm: 0.5
58
+ n_steps: 8
59
+ gae_lambda: 0.9
60
+ vf_coef: 0.4
61
+ gamma: 0.99
62
+ learning_rate: !!float 9.6e-4
63
+ learning_rate_decay: linear
64
+
65
+ HalfCheetahBulletEnv-v0: &pybullet-defaults
66
+ n_timesteps: !!float 2e6
67
+ env_hyperparams:
68
+ n_envs: 4
69
+ normalize: true
70
+ policy_hyperparams:
71
+ use_sde: true
72
+ log_std_init: -2
73
+ init_layers_orthogonal: false
74
+ algo_hyperaparms: &pybullet-algo-defaults
75
+ n_steps: 8
76
+ ent_coef: 0
77
+ max_grad_norm: 0.5
78
+ gae_lambda: 0.9
79
+ gamma: 0.99
80
+ vf_coef: 0.4
81
+ learning_rate: !!float 9.6e-4
82
+ learning_rate_decay: linear
83
+
84
+ AntBulletEnv-v0:
85
+ <<: *pybullet-defaults
86
+
87
+ Walker2DBulletEnv-v0:
88
+ <<: *pybullet-defaults
89
+
90
+ HopperBulletEnv-v0:
91
+ <<: *pybullet-defaults
92
+
93
+ CarRacing-v0:
94
+ n_timesteps: !!float 4e6
95
+ env_hyperparams:
96
+ n_envs: 8
97
+ frame_stack: 4
98
+ policy_hyperparams:
99
+ use_sde: true
100
+ log_std_init: -2
101
+ init_layers_orthogonal: false
102
+ activation_fn: relu
103
+ share_features_extractor: false
104
+ cnn_feature_dim: 256
105
+ hidden_sizes: [256]
106
+ algo_hyperparams:
107
+ n_steps: 8
108
+ learning_rate: !!float 5e-5
109
+ learning_rate_decay: linear
110
+ gamma: 0.99
111
+ gae_lambda: 0.95
112
+ ent_coef: 0
113
+ sde_sample_freq: 4
114
+
115
+ _atari: &atari-defaults
116
+ n_timesteps: !!float 1e7
117
+ env_hyperparams: &atari-env-defaults
118
+ n_envs: 16
119
+ frame_stack: 4
120
+ no_reward_timeout_steps: 1000
121
+ no_reward_fire_steps: 500
122
+ vec_env_class: async
123
+ policy_hyperparams: &atari-policy-defaults
124
+ activation_fn: relu
125
+ algo_hyperparams:
126
+ ent_coef: 0.01
127
+ vf_coef: 0.25
hyperparams/dqn.yml ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ CartPole-v1: &cartpole-defaults
2
+ n_timesteps: !!float 5e4
3
+ env_hyperparams:
4
+ rolling_length: 50
5
+ policy_hyperparams:
6
+ hidden_sizes: [256, 256]
7
+ algo_hyperparams:
8
+ learning_rate: !!float 2.3e-3
9
+ batch_size: 64
10
+ buffer_size: 100000
11
+ learning_starts: 1000
12
+ gamma: 0.99
13
+ target_update_interval: 10
14
+ train_freq: 256
15
+ gradient_steps: 128
16
+ exploration_fraction: 0.16
17
+ exploration_final_eps: 0.04
18
+ eval_params:
19
+ step_freq: !!float 1e4
20
+
21
+ CartPole-v0:
22
+ <<: *cartpole-defaults
23
+ n_timesteps: !!float 4e4
24
+
25
+ MountainCar-v0:
26
+ n_timesteps: !!float 1.2e5
27
+ env_hyperparams:
28
+ rolling_length: 50
29
+ policy_hyperparams:
30
+ hidden_sizes: [256, 256]
31
+ algo_hyperparams:
32
+ learning_rate: !!float 4e-3
33
+ batch_size: 128
34
+ buffer_size: 10000
35
+ learning_starts: 1000
36
+ gamma: 0.98
37
+ target_update_interval: 600
38
+ train_freq: 16
39
+ gradient_steps: 8
40
+ exploration_fraction: 0.2
41
+ exploration_final_eps: 0.07
42
+
43
+ Acrobot-v1:
44
+ n_timesteps: !!float 1e5
45
+ env_hyperparams:
46
+ rolling_length: 50
47
+ policy_hyperparams:
48
+ hidden_sizes: [256, 256]
49
+ algo_hyperparams:
50
+ learning_rate: !!float 6.3e-4
51
+ batch_size: 128
52
+ buffer_size: 50000
53
+ learning_starts: 0
54
+ gamma: 0.99
55
+ target_update_interval: 250
56
+ train_freq: 4
57
+ gradient_steps: -1
58
+ exploration_fraction: 0.12
59
+ exploration_final_eps: 0.1
60
+
61
+ LunarLander-v2:
62
+ n_timesteps: !!float 5e5
63
+ env_hyperparams:
64
+ rolling_length: 50
65
+ policy_hyperparams:
66
+ hidden_sizes: [256, 256]
67
+ algo_hyperparams:
68
+ learning_rate: !!float 1e-4
69
+ batch_size: 256
70
+ buffer_size: 100000
71
+ learning_starts: 10000
72
+ gamma: 0.99
73
+ target_update_interval: 250
74
+ train_freq: 8
75
+ gradient_steps: -1
76
+ exploration_fraction: 0.12
77
+ exploration_final_eps: 0.1
78
+ max_grad_norm: 0.5
79
+ eval_params:
80
+ step_freq: 25_000
81
+
82
+ _atari: &atari-defaults
83
+ n_timesteps: !!float 1e7
84
+ env_hyperparams:
85
+ frame_stack: 4
86
+ no_reward_timeout_steps: 1_000
87
+ no_reward_fire_steps: 500
88
+ n_envs: 8
89
+ vec_env_class: async
90
+ algo_hyperparams:
91
+ buffer_size: 100000
92
+ learning_rate: !!float 1e-4
93
+ batch_size: 32
94
+ learning_starts: 100000
95
+ target_update_interval: 1000
96
+ train_freq: 8
97
+ gradient_steps: 2
98
+ exploration_fraction: 0.1
99
+ exploration_final_eps: 0.01
100
+ eval_params:
101
+ deterministic: false
102
+
103
+ PongNoFrameskip-v4:
104
+ <<: *atari-defaults
105
+ n_timesteps: !!float 2.5e6
106
+
107
+ _impala-atari: &impala-atari-defaults
108
+ <<: *atari-defaults
109
+ policy_hyperparams:
110
+ cnn_style: impala
111
+ cnn_feature_dim: 256
112
+ init_layers_orthogonal: true
113
+ cnn_layers_init_orthogonal: false
114
+
115
+ impala-PongNoFrameskip-v4:
116
+ <<: *impala-atari-defaults
117
+ env_id: PongNoFrameskip-v4
118
+ n_timesteps: !!float 2.5e6
119
+
120
+ impala-BreakoutNoFrameskip-v4:
121
+ <<: *impala-atari-defaults
122
+ env_id: BreakoutNoFrameskip-v4
123
+
124
+ impala-SpaceInvadersNoFrameskip-v4:
125
+ <<: *impala-atari-defaults
126
+ env_id: SpaceInvadersNoFrameskip-v4
127
+
128
+ impala-QbertNoFrameskip-v4:
129
+ <<: *impala-atari-defaults
130
+ env_id: QbertNoFrameskip-v4
hyperparams/ppo.yml ADDED
@@ -0,0 +1,383 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ CartPole-v1: &cartpole-defaults
2
+ n_timesteps: !!float 1e5
3
+ env_hyperparams:
4
+ n_envs: 8
5
+ algo_hyperparams:
6
+ n_steps: 32
7
+ batch_size: 256
8
+ n_epochs: 20
9
+ gae_lambda: 0.8
10
+ gamma: 0.98
11
+ ent_coef: 0.0
12
+ learning_rate: 0.001
13
+ learning_rate_decay: linear
14
+ clip_range: 0.2
15
+ clip_range_decay: linear
16
+ eval_params:
17
+ step_freq: !!float 2.5e4
18
+
19
+ CartPole-v0:
20
+ <<: *cartpole-defaults
21
+ n_timesteps: !!float 5e4
22
+
23
+ MountainCar-v0:
24
+ n_timesteps: !!float 1e6
25
+ env_hyperparams:
26
+ normalize: true
27
+ n_envs: 16
28
+ algo_hyperparams:
29
+ n_steps: 16
30
+ n_epochs: 4
31
+ gae_lambda: 0.98
32
+ gamma: 0.99
33
+ ent_coef: 0.0
34
+
35
+ MountainCarContinuous-v0:
36
+ n_timesteps: !!float 1e5
37
+ env_hyperparams:
38
+ normalize: true
39
+ n_envs: 4
40
+ # policy_hyperparams:
41
+ # init_layers_orthogonal: false
42
+ # log_std_init: -3.29
43
+ # use_sde: true
44
+ algo_hyperparams:
45
+ n_steps: 512
46
+ batch_size: 256
47
+ n_epochs: 10
48
+ learning_rate: !!float 7.77e-5
49
+ ent_coef: 0.01 # 0.00429
50
+ ent_coef_decay: linear
51
+ clip_range: 0.1
52
+ gae_lambda: 0.9
53
+ max_grad_norm: 5
54
+ vf_coef: 0.19
55
+ eval_params:
56
+ step_freq: 5000
57
+
58
+ Acrobot-v1:
59
+ n_timesteps: !!float 1e6
60
+ env_hyperparams:
61
+ n_envs: 16
62
+ normalize: true
63
+ algo_hyperparams:
64
+ n_steps: 256
65
+ n_epochs: 4
66
+ gae_lambda: 0.94
67
+ gamma: 0.99
68
+ ent_coef: 0.0
69
+
70
+ LunarLander-v2:
71
+ n_timesteps: !!float 4e6
72
+ env_hyperparams:
73
+ n_envs: 16
74
+ algo_hyperparams:
75
+ n_steps: 1024
76
+ batch_size: 64
77
+ n_epochs: 4
78
+ gae_lambda: 0.98
79
+ gamma: 0.999
80
+ learning_rate: !!float 5e-4
81
+ learning_rate_decay: linear
82
+ clip_range: 0.2
83
+ clip_range_decay: linear
84
+ ent_coef: 0.01
85
+ normalize_advantage: false
86
+
87
+ BipedalWalker-v3:
88
+ n_timesteps: !!float 10e6
89
+ env_hyperparams:
90
+ n_envs: 16
91
+ normalize: true
92
+ algo_hyperparams:
93
+ n_steps: 2048
94
+ batch_size: 64
95
+ gae_lambda: 0.95
96
+ gamma: 0.99
97
+ n_epochs: 10
98
+ ent_coef: 0.001
99
+ learning_rate: !!float 2.5e-4
100
+ learning_rate_decay: linear
101
+ clip_range: 0.2
102
+ clip_range_decay: linear
103
+
104
+ CarRacing-v0: &carracing-defaults
105
+ n_timesteps: !!float 4e6
106
+ env_hyperparams:
107
+ n_envs: 8
108
+ frame_stack: 4
109
+ policy_hyperparams: &carracing-policy-defaults
110
+ use_sde: true
111
+ log_std_init: -2
112
+ init_layers_orthogonal: false
113
+ activation_fn: relu
114
+ share_features_extractor: false
115
+ cnn_feature_dim: 256
116
+ hidden_sizes: [256]
117
+ algo_hyperparams:
118
+ n_steps: 512
119
+ batch_size: 128
120
+ n_epochs: 10
121
+ learning_rate: !!float 1e-4
122
+ learning_rate_decay: linear
123
+ gamma: 0.99
124
+ gae_lambda: 0.95
125
+ ent_coef: 0.0
126
+ sde_sample_freq: 4
127
+ max_grad_norm: 0.5
128
+ vf_coef: 0.5
129
+ clip_range: 0.2
130
+
131
+ impala-CarRacing-v0:
132
+ <<: *carracing-defaults
133
+ env_id: CarRacing-v0
134
+ policy_hyperparams:
135
+ <<: *carracing-policy-defaults
136
+ cnn_style: impala
137
+ init_layers_orthogonal: true
138
+ cnn_layers_init_orthogonal: false
139
+ hidden_sizes: []
140
+
141
+ # BreakoutNoFrameskip-v4
142
+ # PongNoFrameskip-v4
143
+ # SpaceInvadersNoFrameskip-v4
144
+ # QbertNoFrameskip-v4
145
+ _atari: &atari-defaults
146
+ n_timesteps: !!float 1e7
147
+ env_hyperparams: &atari-env-defaults
148
+ n_envs: 8
149
+ frame_stack: 4
150
+ no_reward_timeout_steps: 1000
151
+ no_reward_fire_steps: 500
152
+ vec_env_class: async
153
+ policy_hyperparams: &atari-policy-defaults
154
+ activation_fn: relu
155
+ algo_hyperparams:
156
+ n_steps: 128
157
+ batch_size: 256
158
+ n_epochs: 4
159
+ learning_rate: !!float 2.5e-4
160
+ learning_rate_decay: linear
161
+ clip_range: 0.1
162
+ clip_range_decay: linear
163
+ vf_coef: 0.5
164
+ ent_coef: 0.01
165
+ eval_params:
166
+ deterministic: false
167
+
168
+ _norm-rewards-atari: &norm-rewards-atari-default
169
+ <<: *atari-defaults
170
+ env_hyperparams:
171
+ <<: *atari-env-defaults
172
+ clip_atari_rewards: false
173
+ normalize: true
174
+ normalize_kwargs:
175
+ norm_obs: false
176
+ norm_reward: true
177
+
178
+ norm-rewards-BreakoutNoFrameskip-v4:
179
+ <<: *norm-rewards-atari-default
180
+ env_id: BreakoutNoFrameskip-v4
181
+
182
+ debug-PongNoFrameskip-v4:
183
+ <<: *atari-defaults
184
+ device: cpu
185
+ env_id: PongNoFrameskip-v4
186
+ env_hyperparams:
187
+ <<: *atari-env-defaults
188
+ vec_env_class: sync
189
+
190
+ _impala-atari: &impala-atari-defaults
191
+ <<: *atari-defaults
192
+ policy_hyperparams:
193
+ <<: *atari-policy-defaults
194
+ cnn_style: impala
195
+ cnn_feature_dim: 256
196
+ init_layers_orthogonal: true
197
+ cnn_layers_init_orthogonal: false
198
+
199
+ impala-PongNoFrameskip-v4:
200
+ <<: *impala-atari-defaults
201
+ env_id: PongNoFrameskip-v4
202
+
203
+ impala-BreakoutNoFrameskip-v4:
204
+ <<: *impala-atari-defaults
205
+ env_id: BreakoutNoFrameskip-v4
206
+
207
+ impala-SpaceInvadersNoFrameskip-v4:
208
+ <<: *impala-atari-defaults
209
+ env_id: SpaceInvadersNoFrameskip-v4
210
+
211
+ impala-QbertNoFrameskip-v4:
212
+ <<: *impala-atari-defaults
213
+ env_id: QbertNoFrameskip-v4
214
+
215
+ HalfCheetahBulletEnv-v0: &pybullet-defaults
216
+ n_timesteps: !!float 2e6
217
+ env_hyperparams: &pybullet-env-defaults
218
+ n_envs: 16
219
+ normalize: true
220
+ policy_hyperparams: &pybullet-policy-defaults
221
+ pi_hidden_sizes: [256, 256]
222
+ v_hidden_sizes: [256, 256]
223
+ activation_fn: relu
224
+ algo_hyperparams: &pybullet-algo-defaults
225
+ n_steps: 512
226
+ batch_size: 128
227
+ n_epochs: 20
228
+ gamma: 0.99
229
+ gae_lambda: 0.9
230
+ ent_coef: 0.0
231
+ max_grad_norm: 0.5
232
+ vf_coef: 0.5
233
+ learning_rate: !!float 3e-5
234
+ clip_range: 0.4
235
+
236
+ AntBulletEnv-v0:
237
+ <<: *pybullet-defaults
238
+ policy_hyperparams:
239
+ <<: *pybullet-policy-defaults
240
+ algo_hyperparams:
241
+ <<: *pybullet-algo-defaults
242
+
243
+ Walker2DBulletEnv-v0:
244
+ <<: *pybullet-defaults
245
+ algo_hyperparams:
246
+ <<: *pybullet-algo-defaults
247
+ clip_range_decay: linear
248
+
249
+ HopperBulletEnv-v0:
250
+ <<: *pybullet-defaults
251
+ algo_hyperparams:
252
+ <<: *pybullet-algo-defaults
253
+ clip_range_decay: linear
254
+
255
+ HumanoidBulletEnv-v0:
256
+ <<: *pybullet-defaults
257
+ n_timesteps: !!float 1e7
258
+ env_hyperparams:
259
+ <<: *pybullet-env-defaults
260
+ n_envs: 8
261
+ policy_hyperparams:
262
+ <<: *pybullet-policy-defaults
263
+ # log_std_init: -1
264
+ algo_hyperparams:
265
+ <<: *pybullet-algo-defaults
266
+ n_steps: 2048
267
+ batch_size: 64
268
+ n_epochs: 10
269
+ gae_lambda: 0.95
270
+ learning_rate: !!float 2.5e-4
271
+ clip_range: 0.2
272
+
273
+ _procgen: &procgen-defaults
274
+ env_hyperparams: &procgen-env-defaults
275
+ env_type: procgen
276
+ n_envs: 64
277
+ # grayscale: false
278
+ # frame_stack: 4
279
+ normalize: true # procgen only normalizes reward
280
+ make_kwargs: &procgen-make-kwargs-defaults
281
+ num_threads: 8
282
+ policy_hyperparams: &procgen-policy-defaults
283
+ activation_fn: relu
284
+ cnn_style: impala
285
+ cnn_feature_dim: 256
286
+ init_layers_orthogonal: true
287
+ cnn_layers_init_orthogonal: false
288
+ algo_hyperparams: &procgen-algo-defaults
289
+ gamma: 0.999
290
+ gae_lambda: 0.95
291
+ n_steps: 256
292
+ batch_size: 2048
293
+ n_epochs: 3
294
+ ent_coef: 0.01
295
+ clip_range: 0.2
296
+ # clip_range_decay: linear
297
+ clip_range_vf: 0.2
298
+ learning_rate: !!float 5e-4
299
+ # learning_rate_decay: linear
300
+ vf_coef: 0.5
301
+ eval_params: &procgen-eval-defaults
302
+ ignore_first_episode: true
303
+ # deterministic: false
304
+ step_freq: !!float 1e5
305
+
306
+ _procgen-easy: &procgen-easy-defaults
307
+ <<: *procgen-defaults
308
+ n_timesteps: !!float 25e6
309
+ env_hyperparams: &procgen-easy-env-defaults
310
+ <<: *procgen-env-defaults
311
+ make_kwargs:
312
+ <<: *procgen-make-kwargs-defaults
313
+ distribution_mode: easy
314
+
315
+ procgen-coinrun-easy: &coinrun-easy-defaults
316
+ <<: *procgen-easy-defaults
317
+ env_id: coinrun
318
+
319
+ debug-procgen-coinrun:
320
+ <<: *coinrun-easy-defaults
321
+ device: cpu
322
+
323
+ procgen-starpilot-easy:
324
+ <<: *procgen-easy-defaults
325
+ env_id: starpilot
326
+
327
+ procgen-bossfight-easy:
328
+ <<: *procgen-easy-defaults
329
+ env_id: bossfight
330
+
331
+ procgen-bigfish-easy:
332
+ <<: *procgen-easy-defaults
333
+ env_id: bigfish
334
+
335
+ _procgen-hard: &procgen-hard-defaults
336
+ <<: *procgen-defaults
337
+ n_timesteps: !!float 200e6
338
+ env_hyperparams: &procgen-hard-env-defaults
339
+ <<: *procgen-env-defaults
340
+ n_envs: 256
341
+ make_kwargs:
342
+ <<: *procgen-make-kwargs-defaults
343
+ distribution_mode: hard
344
+ algo_hyperparams: &procgen-hard-algo-defaults
345
+ <<: *procgen-algo-defaults
346
+ batch_size: 8192
347
+ clip_range_decay: linear
348
+ learning_rate_decay: linear
349
+ eval_params:
350
+ <<: *procgen-eval-defaults
351
+ step_freq: !!float 5e5
352
+
353
+ procgen-starpilot-hard: &procgen-starpilot-hard-defaults
354
+ <<: *procgen-hard-defaults
355
+ env_id: starpilot
356
+
357
+ procgen-starpilot-hard-2xIMPALA:
358
+ <<: *procgen-starpilot-hard-defaults
359
+ policy_hyperparams:
360
+ <<: *procgen-policy-defaults
361
+ impala_channels: [32, 64, 64]
362
+ algo_hyperparams:
363
+ <<: *procgen-hard-algo-defaults
364
+ learning_rate: !!float 3.3e-4
365
+
366
+ procgen-starpilot-hard-2xIMPALA-fat:
367
+ <<: *procgen-starpilot-hard-defaults
368
+ policy_hyperparams:
369
+ <<: *procgen-policy-defaults
370
+ impala_channels: [32, 64, 64]
371
+ cnn_feature_dim: 512
372
+ algo_hyperparams:
373
+ <<: *procgen-hard-algo-defaults
374
+ learning_rate: !!float 2.5e-4
375
+
376
+ procgen-starpilot-hard-4xIMPALA:
377
+ <<: *procgen-starpilot-hard-defaults
378
+ policy_hyperparams:
379
+ <<: *procgen-policy-defaults
380
+ impala_channels: [64, 128, 128]
381
+ algo_hyperparams:
382
+ <<: *procgen-hard-algo-defaults
383
+ learning_rate: !!float 2.1e-4
hyperparams/vpg.yml ADDED
@@ -0,0 +1,197 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ CartPole-v1: &cartpole-defaults
2
+ n_timesteps: !!float 4e5
3
+ algo_hyperparams:
4
+ n_steps: 4096
5
+ pi_lr: 0.01
6
+ gamma: 0.99
7
+ gae_lambda: 1
8
+ val_lr: 0.01
9
+ train_v_iters: 80
10
+ eval_params:
11
+ step_freq: !!float 2.5e4
12
+
13
+ CartPole-v0:
14
+ <<: *cartpole-defaults
15
+ n_timesteps: !!float 1e5
16
+ algo_hyperparams:
17
+ n_steps: 1024
18
+ pi_lr: 0.01
19
+ gamma: 0.99
20
+ gae_lambda: 1
21
+ val_lr: 0.01
22
+ train_v_iters: 80
23
+
24
+ MountainCar-v0:
25
+ n_timesteps: !!float 1e6
26
+ env_hyperparams:
27
+ normalize: true
28
+ n_envs: 16
29
+ algo_hyperparams:
30
+ n_steps: 200
31
+ pi_lr: 0.005
32
+ gamma: 0.99
33
+ gae_lambda: 0.97
34
+ val_lr: 0.01
35
+ train_v_iters: 80
36
+ max_grad_norm: 0.5
37
+
38
+ MountainCarContinuous-v0:
39
+ n_timesteps: !!float 3e5
40
+ env_hyperparams:
41
+ normalize: true
42
+ n_envs: 4
43
+ # policy_hyperparams:
44
+ # init_layers_orthogonal: false
45
+ # log_std_init: -3.29
46
+ # use_sde: true
47
+ algo_hyperparams:
48
+ n_steps: 1000
49
+ pi_lr: !!float 5e-4
50
+ gamma: 0.99
51
+ gae_lambda: 0.9
52
+ val_lr: !!float 1e-3
53
+ train_v_iters: 80
54
+ max_grad_norm: 5
55
+ eval_params:
56
+ step_freq: 5000
57
+
58
+ Acrobot-v1:
59
+ n_timesteps: !!float 2e5
60
+ algo_hyperparams:
61
+ n_steps: 2048
62
+ pi_lr: 0.005
63
+ gamma: 0.99
64
+ gae_lambda: 0.97
65
+ val_lr: 0.01
66
+ train_v_iters: 80
67
+ max_grad_norm: 0.5
68
+
69
+ LunarLander-v2:
70
+ n_timesteps: !!float 4e6
71
+ policy_hyperparams:
72
+ hidden_sizes: [256, 256]
73
+ algo_hyperparams:
74
+ n_steps: 2048
75
+ pi_lr: 0.0001
76
+ gamma: 0.999
77
+ gae_lambda: 0.97
78
+ val_lr: 0.0001
79
+ train_v_iters: 80
80
+ max_grad_norm: 0.5
81
+ eval_params:
82
+ deterministic: false
83
+
84
+ BipedalWalker-v3:
85
+ n_timesteps: !!float 10e6
86
+ env_hyperparams:
87
+ n_envs: 16
88
+ normalize: true
89
+ policy_hyperparams:
90
+ hidden_sizes: [256, 256]
91
+ algo_hyperparams:
92
+ n_steps: 1600
93
+ gae_lambda: 0.95
94
+ gamma: 0.99
95
+ pi_lr: !!float 1e-4
96
+ val_lr: !!float 1e-4
97
+ train_v_iters: 80
98
+ max_grad_norm: 0.5
99
+ eval_params:
100
+ deterministic: false
101
+
102
+ CarRacing-v0:
103
+ n_timesteps: !!float 4e6
104
+ env_hyperparams:
105
+ frame_stack: 4
106
+ n_envs: 4
107
+ vec_env_class: sync
108
+ policy_hyperparams:
109
+ use_sde: true
110
+ log_std_init: -2
111
+ init_layers_orthogonal: false
112
+ activation_fn: relu
113
+ cnn_feature_dim: 256
114
+ hidden_sizes: [256]
115
+ algo_hyperparams:
116
+ n_steps: 1000
117
+ pi_lr: !!float 5e-5
118
+ gamma: 0.99
119
+ gae_lambda: 0.95
120
+ val_lr: !!float 1e-4
121
+ train_v_iters: 40
122
+ max_grad_norm: 0.5
123
+ sde_sample_freq: 4
124
+
125
+ HalfCheetahBulletEnv-v0: &pybullet-defaults
126
+ n_timesteps: !!float 2e6
127
+ env_hyperparams: &pybullet-env-defaults
128
+ normalize: true
129
+ policy_hyperparams: &pybullet-policy-defaults
130
+ hidden_sizes: [256, 256]
131
+ algo_hyperparams: &pybullet-algo-defaults
132
+ n_steps: 4000
133
+ pi_lr: !!float 3e-4
134
+ gamma: 0.99
135
+ gae_lambda: 0.97
136
+ val_lr: !!float 1e-3
137
+ train_v_iters: 80
138
+ max_grad_norm: 0.5
139
+
140
+ AntBulletEnv-v0:
141
+ <<: *pybullet-defaults
142
+ policy_hyperparams:
143
+ <<: *pybullet-policy-defaults
144
+ hidden_sizes: [400, 300]
145
+ algo_hyperparams:
146
+ <<: *pybullet-algo-defaults
147
+ pi_lr: !!float 7e-4
148
+ val_lr: !!float 7e-3
149
+
150
+ HopperBulletEnv-v0:
151
+ <<: *pybullet-defaults
152
+
153
+ Walker2DBulletEnv-v0:
154
+ <<: *pybullet-defaults
155
+
156
+ FrozenLake-v1:
157
+ n_timesteps: !!float 8e5
158
+ env_params:
159
+ make_kwargs:
160
+ map_name: 8x8
161
+ is_slippery: true
162
+ policy_hyperparams:
163
+ hidden_sizes: [64]
164
+ algo_hyperparams:
165
+ n_steps: 2048
166
+ pi_lr: 0.01
167
+ gamma: 0.99
168
+ gae_lambda: 0.98
169
+ val_lr: 0.01
170
+ train_v_iters: 80
171
+ max_grad_norm: 0.5
172
+ eval_params:
173
+ step_freq: !!float 5e4
174
+ n_episodes: 10
175
+ save_best: true
176
+
177
+ _atari: &atari-defaults
178
+ n_timesteps: !!float 25e6
179
+ env_hyperparams:
180
+ n_envs: 4
181
+ frame_stack: 4
182
+ no_reward_timeout_steps: 1000
183
+ no_reward_fire_steps: 500
184
+ vec_env_class: async
185
+ policy_hyperparams:
186
+ activation_fn: relu
187
+ algo_hyperparams:
188
+ n_steps: 2048
189
+ pi_lr: !!float 5e-5
190
+ gamma: 0.99
191
+ gae_lambda: 0.95
192
+ val_lr: !!float 1e-4
193
+ train_v_iters: 80
194
+ max_grad_norm: 0.5
195
+ ent_coef: 0.01
196
+ eval_params:
197
+ deterministic: false
lambda_labs/benchmark.sh ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+
3
+ # export WANDB_PROJECT_NAME="rl-algo-impls"
4
+
5
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-6}"
6
+
7
+ ALGOS=(
8
+ # "vpg"
9
+ # "dqn"
10
+ # "ppo"
11
+ "a2c"
12
+ )
13
+ ENVS=(
14
+ # Basic
15
+ "CartPole-v1"
16
+ "MountainCar-v0"
17
+ "MountainCarContinuous-v0"
18
+ "Acrobot-v1"
19
+ "LunarLander-v2"
20
+ "BipedalWalker-v3"
21
+ # PyBullet
22
+ "HalfCheetahBulletEnv-v0"
23
+ "AntBulletEnv-v0"
24
+ "HopperBulletEnv-v0"
25
+ "Walker2DBulletEnv-v0"
26
+ # CarRacing
27
+ "CarRacing-v0"
28
+ # Atari
29
+ "PongNoFrameskip-v4"
30
+ "BreakoutNoFrameskip-v4"
31
+ "SpaceInvadersNoFrameskip-v4"
32
+ "QbertNoFrameskip-v4"
33
+ )
34
+ train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
lambda_labs/impala_atari_benchmark.sh ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+
3
+ # export WANDB_PROJECT_NAME="rl-algo-impls"
4
+
5
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-5}"
6
+
7
+ ALGOS=(
8
+ # "vpg"
9
+ # "dqn"
10
+ "ppo"
11
+ )
12
+ ENVS=(
13
+ "impala-PongNoFrameskip-v4"
14
+ "impala-BreakoutNoFrameskip-v4"
15
+ "impala-SpaceInvadersNoFrameskip-v4"
16
+ "impala-QbertNoFrameskip-v4"
17
+ "impala-CarRacing-v0"
18
+ )
19
+ train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
lambda_labs/lambda_requirements.txt ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ scipy >= 1.10.0, < 1.11
2
+ tensorboard >= ^2.11.0, < 2.12
3
+ AutoROM.accept-rom-license >= 0.4.2, < 0.5
4
+ stable-baselines3[extra] >= 1.7.0, < 1.8
5
+ gym[box2d] >= 0.21.0, < 0.22
6
+ pyglet == 1.5.27
7
+ wandb >= 0.13.10, < 0.14
8
+ pyvirtualdisplay == 3.0
9
+ pybullet >= 3.2.5, < 3.3
10
+ tabulate >= 0.9.0, < 0.10
11
+ huggingface-hub >= 0.12.0, < 0.13
12
+ numexpr >= 2.8.4, < 2.9
13
+ gym3 >= 0.3.3, < 0.4
14
+ glfw >= 1.12.0, < 1.13
15
+ procgen >= 0.10.7, < 0.11
16
+ ipython >= 8.10.0, < 8.11
lambda_labs/procgen_benchmark.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+
3
+ # export WANDB_PROJECT_NAME="rl-algo-impls"
4
+
5
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
6
+
7
+ ALGOS=(
8
+ # "vpg"
9
+ # "dqn"
10
+ "ppo"
11
+ )
12
+ ENVS=(
13
+ "procgen-coinrun-easy"
14
+ "procgen-starpilot-easy"
15
+ "procgen-bossfight-easy"
16
+ "procgen-bigfish-easy"
17
+ )
18
+ train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
lambda_labs/setup.sh ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ sudo apt update
2
+ sudo apt install -y python-opengl
3
+ sudo apt install -y ffmpeg
4
+ sudo apt install -y xvfb
5
+ sudo apt install -y swig
6
+
7
+ python3 -m pip install --upgrade pip
8
+ pip install --upgrade torch torchvision torchaudio
9
+
10
+ pip install --upgrade -r ~/rl-algo-impls/lambda_labs/lambda_requirements.txt
lambda_labs/starpilot_hard_benchmark.sh ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ source benchmarks/train_loop.sh
2
+
3
+ # export WANDB_PROJECT_NAME="rl-algo-impls"
4
+
5
+ BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-1}"
6
+
7
+ ALGOS=(
8
+ "ppo"
9
+ )
10
+ ENVS=(
11
+ "procgen-starpilot-hard"
12
+ "procgen-starpilot-hard-2xIMPALA"
13
+ "procgen-starpilot-hard-2xIMPALA-fat"
14
+ "procgen-starpilot-hard-4xIMPALA"
15
+ )
16
+ train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
poetry.lock ADDED
The diff for this file is too large to render. See raw diff
 
ppo/ppo.py ADDED
@@ -0,0 +1,349 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import torch
3
+ import torch.nn as nn
4
+ import torch.nn.functional as F
5
+
6
+ from dataclasses import asdict, dataclass, field
7
+ from time import perf_counter
8
+ from torch.optim import Adam
9
+ from torch.utils.tensorboard.writer import SummaryWriter
10
+ from typing import List, Optional, NamedTuple, TypeVar
11
+
12
+ from shared.algorithm import Algorithm
13
+ from shared.callbacks.callback import Callback
14
+ from shared.gae import compute_advantage, compute_rtg_and_advantage
15
+ from shared.policy.on_policy import ActorCritic
16
+ from shared.schedule import constant_schedule, linear_schedule, update_learning_rate
17
+ from shared.trajectory import Trajectory, TrajectoryAccumulator
18
+ from wrappers.vectorable_wrapper import VecEnv, VecEnvObs
19
+
20
+
21
+ @dataclass
22
+ class PPOTrajectory(Trajectory):
23
+ logp_a: List[float] = field(default_factory=list)
24
+
25
+ def add(
26
+ self,
27
+ obs: np.ndarray,
28
+ act: np.ndarray,
29
+ next_obs: np.ndarray,
30
+ rew: float,
31
+ terminated: bool,
32
+ v: float,
33
+ logp_a: float,
34
+ ):
35
+ super().add(obs, act, next_obs, rew, terminated, v)
36
+ self.logp_a.append(logp_a)
37
+
38
+
39
+ class PPOTrajectoryAccumulator(TrajectoryAccumulator):
40
+ def __init__(self, num_envs: int) -> None:
41
+ super().__init__(num_envs, PPOTrajectory)
42
+
43
+ def step(
44
+ self,
45
+ obs: VecEnvObs,
46
+ action: np.ndarray,
47
+ next_obs: VecEnvObs,
48
+ reward: np.ndarray,
49
+ done: np.ndarray,
50
+ val: np.ndarray,
51
+ logp_a: np.ndarray,
52
+ ) -> None:
53
+ super().step(obs, action, next_obs, reward, done, val, logp_a)
54
+
55
+
56
+ class TrainStepStats(NamedTuple):
57
+ loss: float
58
+ pi_loss: float
59
+ v_loss: float
60
+ entropy_loss: float
61
+ approx_kl: float
62
+ clipped_frac: float
63
+ val_clipped_frac: float
64
+
65
+
66
+ @dataclass
67
+ class TrainStats:
68
+ loss: float
69
+ pi_loss: float
70
+ v_loss: float
71
+ entropy_loss: float
72
+ approx_kl: float
73
+ clipped_frac: float
74
+ val_clipped_frac: float
75
+ explained_var: float
76
+
77
+ def __init__(self, step_stats: List[TrainStepStats], explained_var: float) -> None:
78
+ self.loss = np.mean([s.loss for s in step_stats]).item()
79
+ self.pi_loss = np.mean([s.pi_loss for s in step_stats]).item()
80
+ self.v_loss = np.mean([s.v_loss for s in step_stats]).item()
81
+ self.entropy_loss = np.mean([s.entropy_loss for s in step_stats]).item()
82
+ self.approx_kl = np.mean([s.approx_kl for s in step_stats]).item()
83
+ self.clipped_frac = np.mean([s.clipped_frac for s in step_stats]).item()
84
+ self.val_clipped_frac = np.mean([s.val_clipped_frac for s in step_stats]).item()
85
+ self.explained_var = explained_var
86
+
87
+ def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None:
88
+ for name, value in asdict(self).items():
89
+ tb_writer.add_scalar(f"losses/{name}", value, global_step=global_step)
90
+
91
+ def __repr__(self) -> str:
92
+ return " | ".join(
93
+ [
94
+ f"Loss: {round(self.loss, 2)}",
95
+ f"Pi L: {round(self.pi_loss, 2)}",
96
+ f"V L: {round(self.v_loss, 2)}",
97
+ f"E L: {round(self.entropy_loss, 2)}",
98
+ f"Apx KL Div: {round(self.approx_kl, 2)}",
99
+ f"Clip Frac: {round(self.clipped_frac, 2)}",
100
+ f"Val Clip Frac: {round(self.val_clipped_frac, 2)}",
101
+ ]
102
+ )
103
+
104
+
105
+ PPOSelf = TypeVar("PPOSelf", bound="PPO")
106
+
107
+
108
+ class PPO(Algorithm):
109
+ def __init__(
110
+ self,
111
+ policy: ActorCritic,
112
+ env: VecEnv,
113
+ device: torch.device,
114
+ tb_writer: SummaryWriter,
115
+ learning_rate: float = 3e-4,
116
+ learning_rate_decay: str = "none",
117
+ n_steps: int = 2048,
118
+ batch_size: int = 64,
119
+ n_epochs: int = 10,
120
+ gamma: float = 0.99,
121
+ gae_lambda: float = 0.95,
122
+ clip_range: float = 0.2,
123
+ clip_range_decay: str = "none",
124
+ clip_range_vf: Optional[float] = None,
125
+ clip_range_vf_decay: str = "none",
126
+ normalize_advantage: bool = True,
127
+ ent_coef: float = 0.0,
128
+ ent_coef_decay: str = "none",
129
+ vf_coef: float = 0.5,
130
+ ppo2_vf_coef_halving: bool = False,
131
+ max_grad_norm: float = 0.5,
132
+ update_rtg_between_epochs: bool = False,
133
+ sde_sample_freq: int = -1,
134
+ ) -> None:
135
+ super().__init__(policy, env, device, tb_writer)
136
+ self.policy = policy
137
+
138
+ self.gamma = gamma
139
+ self.gae_lambda = gae_lambda
140
+ self.optimizer = Adam(self.policy.parameters(), lr=learning_rate, eps=1e-7)
141
+ self.lr_schedule = (
142
+ linear_schedule(learning_rate, 0)
143
+ if learning_rate_decay == "linear"
144
+ else constant_schedule(learning_rate)
145
+ )
146
+ self.max_grad_norm = max_grad_norm
147
+ self.clip_range_schedule = (
148
+ linear_schedule(clip_range, 0)
149
+ if clip_range_decay == "linear"
150
+ else constant_schedule(clip_range)
151
+ )
152
+ self.clip_range_vf_schedule = None
153
+ if clip_range_vf:
154
+ self.clip_range_vf_schedule = (
155
+ linear_schedule(clip_range_vf, 0)
156
+ if clip_range_vf_decay == "linear"
157
+ else constant_schedule(clip_range_vf)
158
+ )
159
+ self.normalize_advantage = normalize_advantage
160
+ self.ent_coef_schedule = (
161
+ linear_schedule(ent_coef, 0)
162
+ if ent_coef_decay == "linear"
163
+ else constant_schedule(ent_coef)
164
+ )
165
+ self.vf_coef = vf_coef
166
+ self.ppo2_vf_coef_halving = ppo2_vf_coef_halving
167
+
168
+ self.n_steps = n_steps
169
+ self.batch_size = batch_size
170
+ self.n_epochs = n_epochs
171
+ self.sde_sample_freq = sde_sample_freq
172
+
173
+ self.update_rtg_between_epochs = update_rtg_between_epochs
174
+
175
+ def learn(
176
+ self: PPOSelf,
177
+ total_timesteps: int,
178
+ callback: Optional[Callback] = None,
179
+ ) -> PPOSelf:
180
+ obs = self.env.reset()
181
+ ts_elapsed = 0
182
+ while ts_elapsed < total_timesteps:
183
+ start_time = perf_counter()
184
+ accumulator = self._collect_trajectories(obs)
185
+ rollout_steps = self.n_steps * self.env.num_envs
186
+ ts_elapsed += rollout_steps
187
+ progress = ts_elapsed / total_timesteps
188
+ train_stats = self.train(accumulator.all_trajectories, progress, ts_elapsed)
189
+ train_stats.write_to_tensorboard(self.tb_writer, ts_elapsed)
190
+ end_time = perf_counter()
191
+ self.tb_writer.add_scalar(
192
+ "train/steps_per_second",
193
+ rollout_steps / (end_time - start_time),
194
+ ts_elapsed,
195
+ )
196
+ if callback:
197
+ callback.on_step(timesteps_elapsed=rollout_steps)
198
+
199
+ return self
200
+
201
+ def _collect_trajectories(self, obs: VecEnvObs) -> PPOTrajectoryAccumulator:
202
+ self.policy.eval()
203
+ accumulator = PPOTrajectoryAccumulator(self.env.num_envs)
204
+ self.policy.reset_noise()
205
+ for i in range(self.n_steps):
206
+ if self.sde_sample_freq > 0 and i > 0 and i % self.sde_sample_freq == 0:
207
+ self.policy.reset_noise()
208
+ action, value, logp_a, clamped_action = self.policy.step(obs)
209
+ next_obs, reward, done, _ = self.env.step(clamped_action)
210
+ accumulator.step(obs, action, next_obs, reward, done, value, logp_a)
211
+ obs = next_obs
212
+ return accumulator
213
+
214
+ def train(
215
+ self, trajectories: List[PPOTrajectory], progress: float, timesteps_elapsed: int
216
+ ) -> TrainStats:
217
+ self.policy.train()
218
+ learning_rate = self.lr_schedule(progress)
219
+ update_learning_rate(self.optimizer, learning_rate)
220
+ self.tb_writer.add_scalar(
221
+ "charts/learning_rate",
222
+ self.optimizer.param_groups[0]["lr"],
223
+ timesteps_elapsed,
224
+ )
225
+
226
+ pi_clip = self.clip_range_schedule(progress)
227
+ self.tb_writer.add_scalar("charts/pi_clip", pi_clip, timesteps_elapsed)
228
+ if self.clip_range_vf_schedule:
229
+ v_clip = self.clip_range_vf_schedule(progress)
230
+ self.tb_writer.add_scalar("charts/v_clip", v_clip, timesteps_elapsed)
231
+ else:
232
+ v_clip = None
233
+ ent_coef = self.ent_coef_schedule(progress)
234
+ self.tb_writer.add_scalar("charts/ent_coef", ent_coef, timesteps_elapsed)
235
+
236
+ obs = torch.as_tensor(
237
+ np.concatenate([np.array(t.obs) for t in trajectories]), device=self.device
238
+ )
239
+ act = torch.as_tensor(
240
+ np.concatenate([np.array(t.act) for t in trajectories]), device=self.device
241
+ )
242
+ rtg, adv = compute_rtg_and_advantage(
243
+ trajectories, self.policy, self.gamma, self.gae_lambda, self.device
244
+ )
245
+ orig_v = torch.as_tensor(
246
+ np.concatenate([np.array(t.v) for t in trajectories]), device=self.device
247
+ )
248
+ orig_logp_a = torch.as_tensor(
249
+ np.concatenate([np.array(t.logp_a) for t in trajectories]),
250
+ device=self.device,
251
+ )
252
+
253
+ step_stats = []
254
+ for _ in range(self.n_epochs):
255
+ step_stats.clear()
256
+ if self.update_rtg_between_epochs:
257
+ rtg, adv = compute_rtg_and_advantage(
258
+ trajectories, self.policy, self.gamma, self.gae_lambda, self.device
259
+ )
260
+ else:
261
+ adv = compute_advantage(
262
+ trajectories, self.policy, self.gamma, self.gae_lambda, self.device
263
+ )
264
+ idxs = torch.randperm(len(obs))
265
+ for i in range(0, len(obs), self.batch_size):
266
+ mb_idxs = idxs[i : i + self.batch_size]
267
+ mb_adv = adv[mb_idxs]
268
+ if self.normalize_advantage:
269
+ mb_adv = (mb_adv - mb_adv.mean(-1)) / (mb_adv.std(-1) + 1e-8)
270
+ step_stats.append(
271
+ self._train_step(
272
+ pi_clip,
273
+ v_clip,
274
+ ent_coef,
275
+ obs[mb_idxs],
276
+ act[mb_idxs],
277
+ rtg[mb_idxs],
278
+ mb_adv,
279
+ orig_v[mb_idxs],
280
+ orig_logp_a[mb_idxs],
281
+ )
282
+ )
283
+
284
+ y_pred, y_true = orig_v.cpu().numpy(), rtg.cpu().numpy()
285
+ var_y = np.var(y_true).item()
286
+ explained_var = (
287
+ np.nan if var_y == 0 else 1 - np.var(y_true - y_pred).item() / var_y
288
+ )
289
+
290
+ return TrainStats(step_stats, explained_var)
291
+
292
+ def _train_step(
293
+ self,
294
+ pi_clip: float,
295
+ v_clip: Optional[float],
296
+ ent_coef: float,
297
+ obs: torch.Tensor,
298
+ act: torch.Tensor,
299
+ rtg: torch.Tensor,
300
+ adv: torch.Tensor,
301
+ orig_v: torch.Tensor,
302
+ orig_logp_a: torch.Tensor,
303
+ ) -> TrainStepStats:
304
+ logp_a, entropy, v = self.policy(obs, act)
305
+ logratio = logp_a - orig_logp_a
306
+ ratio = torch.exp(logratio)
307
+ clip_ratio = torch.clamp(ratio, min=1 - pi_clip, max=1 + pi_clip)
308
+ pi_loss = torch.maximum(-ratio * adv, -clip_ratio * adv).mean()
309
+
310
+ v_loss_unclipped = (v - rtg) ** 2
311
+ if v_clip:
312
+ v_loss_clipped = (
313
+ orig_v + torch.clamp(v - orig_v, -v_clip, v_clip) - rtg
314
+ ) ** 2
315
+ v_loss = torch.max(v_loss_unclipped, v_loss_clipped).mean()
316
+ else:
317
+ v_loss = v_loss_unclipped.mean()
318
+ if self.ppo2_vf_coef_halving:
319
+ v_loss *= 0.5
320
+
321
+ entropy_loss = -entropy.mean()
322
+
323
+ loss = pi_loss + ent_coef * entropy_loss + self.vf_coef * v_loss
324
+
325
+ self.optimizer.zero_grad()
326
+ loss.backward()
327
+ nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
328
+ self.optimizer.step()
329
+
330
+ with torch.no_grad():
331
+ approx_kl = ((ratio - 1) - logratio).mean().cpu().numpy().item()
332
+ clipped_frac = (
333
+ ((ratio - 1).abs() > pi_clip).float().mean().cpu().numpy().item()
334
+ )
335
+ val_clipped_frac = (
336
+ (((v - orig_v).abs() > v_clip).float().mean().cpu().numpy().item())
337
+ if v_clip
338
+ else 0
339
+ )
340
+
341
+ return TrainStepStats(
342
+ loss.item(),
343
+ pi_loss.item(),
344
+ v_loss.item(),
345
+ entropy_loss.item(),
346
+ approx_kl,
347
+ clipped_frac,
348
+ val_clipped_frac,
349
+ )
publish/markdown_format.py ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import pandas as pd
3
+ import wandb.apis.public
4
+ import yaml
5
+
6
+ from collections import defaultdict
7
+ from dataclasses import dataclass, asdict
8
+ from typing import Any, Dict, Iterable, List, NamedTuple, Optional, TypeVar
9
+ from urllib.parse import urlparse
10
+
11
+ from runner.evaluate import Evaluation
12
+
13
+ EvaluationRowSelf = TypeVar("EvaluationRowSelf", bound="EvaluationRow")
14
+
15
+
16
+ @dataclass
17
+ class EvaluationRow:
18
+ algo: str
19
+ env: str
20
+ seed: Optional[int]
21
+ reward_mean: float
22
+ reward_std: float
23
+ eval_episodes: int
24
+ best: str
25
+ wandb_url: str
26
+
27
+ @staticmethod
28
+ def data_frame(rows: List[EvaluationRowSelf]) -> pd.DataFrame:
29
+ results = defaultdict(list)
30
+ for r in rows:
31
+ for k, v in asdict(r).items():
32
+ results[k].append(v)
33
+ return pd.DataFrame(results)
34
+
35
+
36
+ class EvalTableData(NamedTuple):
37
+ run: wandb.apis.public.Run
38
+ evaluation: Evaluation
39
+
40
+
41
+ def evaluation_table(table_data: Iterable[EvalTableData]) -> str:
42
+ best_stats = sorted(
43
+ [d.evaluation.stats for d in table_data], key=lambda r: r.score, reverse=True
44
+ )[0]
45
+ table_data = sorted(table_data, key=lambda d: d.evaluation.config.seed() or 0)
46
+ rows = [
47
+ EvaluationRow(
48
+ config.algo,
49
+ config.env_id,
50
+ config.seed(),
51
+ stats.score.mean,
52
+ stats.score.std,
53
+ len(stats),
54
+ "*" if stats == best_stats else "",
55
+ f"[wandb]({r.url})",
56
+ )
57
+ for (r, (_, stats, config)) in table_data
58
+ ]
59
+ df = EvaluationRow.data_frame(rows)
60
+ return df.to_markdown(index=False)
61
+
62
+
63
+ def github_project_link(github_url: str) -> str:
64
+ return f"[{urlparse(github_url).path}]({github_url})"
65
+
66
+
67
+ def header_section(algo: str, env: str, github_url: str, wandb_report_url: str) -> str:
68
+ algo_caps = algo.upper()
69
+ lines = [
70
+ f"# **{algo_caps}** Agent playing **{env}**",
71
+ f"This is a trained model of a **{algo_caps}** agent playing **{env}** using "
72
+ f"the {github_project_link(github_url)} repo.",
73
+ f"All models trained at this commit can be found at {wandb_report_url}.",
74
+ ]
75
+ return "\n\n".join(lines)
76
+
77
+
78
+ def github_tree_link(github_url: str, commit_hash: Optional[str]) -> str:
79
+ if not commit_hash:
80
+ return github_project_link(github_url)
81
+ return f"[{commit_hash[:7]}]({github_url}/tree/{commit_hash})"
82
+
83
+
84
+ def results_section(
85
+ table_data: List[EvalTableData], algo: str, github_url: str, commit_hash: str
86
+ ) -> str:
87
+ # type: ignore
88
+ lines = [
89
+ "## Training Results",
90
+ f"This model was trained from {len(table_data)} trainings of **{algo.upper()}** "
91
+ + "agents using different initial seeds. "
92
+ + f"These agents were trained by checking out "
93
+ + f"{github_tree_link(github_url, commit_hash)}. "
94
+ + "The best and last models were kept from each training. "
95
+ + "This submission has loaded the best models from each training, reevaluates "
96
+ + "them, and selects the best model from these latest evaluations (mean - std).",
97
+ ]
98
+ lines.append(evaluation_table(table_data))
99
+ return "\n\n".join(lines)
100
+
101
+
102
+ def prerequisites_section() -> str:
103
+ return """
104
+ ### Prerequisites: Weights & Biases (WandB)
105
+ Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
106
+ By default training goes to a rl-algo-impls project while benchmarks go to
107
+ rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
108
+ models and the model weights are uploaded to WandB.
109
+
110
+ Before doing anything below, you'll need to create a wandb account and run `wandb
111
+ login`.
112
+ """
113
+
114
+
115
+ def usage_section(github_url: str, run_path: str, commit_hash: str) -> str:
116
+ return f"""
117
+ ## Usage
118
+ {urlparse(github_url).path}: {github_url}
119
+
120
+ Note: While the model state dictionary and hyperaparameters are saved, the latest
121
+ implementation could be sufficiently different to not be able to reproduce similar
122
+ results. You might need to checkout the commit the agent was trained on:
123
+ {github_tree_link(github_url, commit_hash)}.
124
+ ```
125
+ # Downloads the model, sets hyperparameters, and runs agent for 3 episodes
126
+ python enjoy.py --wandb-run-path={run_path}
127
+ ```
128
+
129
+ Setup hasn't been completely worked out yet, so you might be best served by using Google
130
+ Colab starting from the
131
+ [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
132
+ notebook.
133
+ """
134
+
135
+
136
+ def training_setion(
137
+ github_url: str, commit_hash: str, algo: str, env: str, seed: Optional[int]
138
+ ) -> str:
139
+ return f"""
140
+ ## Training
141
+ If you want the highest chance to reproduce these results, you'll want to checkout the
142
+ commit the agent was trained on: {github_tree_link(github_url, commit_hash)}. While
143
+ training is deterministic, different hardware will give different results.
144
+
145
+ ```
146
+ python train.py --algo {algo} --env {env} {'--seed ' + str(seed) if seed is not None else ''}
147
+ ```
148
+
149
+ Setup hasn't been completely worked out yet, so you might be best served by using Google
150
+ Colab starting from the
151
+ [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
152
+ notebook.
153
+ """
154
+
155
+
156
+ def benchmarking_section(report_url: str) -> str:
157
+ return f"""
158
+ ## Benchmarking (with Lambda Labs instance)
159
+ This and other models from {report_url} were generated by running a script on a Lambda
160
+ Labs instance. In a Lambda Labs instance terminal:
161
+ ```
162
+ git clone git@github.com:sgoodfriend/rl-algo-impls.git
163
+ cd rl-algo-impls
164
+ bash ./lambda_labs/setup.sh
165
+ wandb login
166
+ bash ./lambda_labs/benchmark.sh
167
+ ```
168
+
169
+ ### Alternative: Google Colab Pro+
170
+ As an alternative,
171
+ [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
172
+ can be used. However, this requires a Google Colab Pro+ subscription and running across
173
+ 4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
174
+ """
175
+
176
+
177
+ def hyperparams_section(run_config: Dict[str, Any]) -> str:
178
+ return f"""
179
+ ## Hyperparameters
180
+ This isn't exactly the format of hyperparams in {os.path.join("hyperparams",
181
+ run_config["algo"] + ".yml")}, but instead the Wandb Run Config. However, it's very
182
+ close and has some additional data:
183
+ ```
184
+ {yaml.dump(run_config)}
185
+ ```
186
+ """
187
+
188
+
189
+ def model_card_text(
190
+ algo: str,
191
+ env: str,
192
+ github_url: str,
193
+ commit_hash: str,
194
+ wandb_report_url: str,
195
+ table_data: List[EvalTableData],
196
+ best_eval: EvalTableData,
197
+ ) -> str:
198
+ run, (_, _, config) = best_eval
199
+ run_path = "/".join(run.path)
200
+ return "\n\n".join(
201
+ [
202
+ header_section(algo, env, github_url, wandb_report_url),
203
+ results_section(table_data, algo, github_url, commit_hash),
204
+ prerequisites_section(),
205
+ usage_section(github_url, run_path, commit_hash),
206
+ training_setion(github_url, commit_hash, algo, env, config.seed()),
207
+ benchmarking_section(wandb_report_url),
208
+ hyperparams_section(run.config),
209
+ ]
210
+ )
pyproject.toml ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [tool.poetry]
2
+ name = "rl-algo-impls"
3
+ version = "0.1.0"
4
+ description = "Implementations of reinforcement learning algorithms"
5
+ authors = ["Scott Goodfriend <goodfriend.scott@gmail.com>"]
6
+ license = "MIT License"
7
+ readme = "README.md"
8
+ packages = [{include = "rl_algo_impls"}]
9
+
10
+ [tool.poetry.dependencies]
11
+ python = "~3.10"
12
+ "AutoROM.accept-rom-license" = "^0.4.2"
13
+ stable-baselines3 = {extras = ["extra"], version = "^1.7.0"}
14
+ scipy = "^1.10.0"
15
+ gym = {extras = ["box2d"], version = "^0.21.0"}
16
+ pyglet = "1.5.27"
17
+ PyYAML = "^6.0"
18
+ tensorboard = "^2.11.0"
19
+ pybullet = "^3.2.5"
20
+ wandb = "^0.13.9"
21
+ conda-lock = "^1.3.0"
22
+ torch-tb-profiler = "^0.4.1"
23
+ jupyter = "^1.0.0"
24
+ tabulate = "^0.9.0"
25
+ huggingface-hub = "^0.12.0"
26
+ cryptography = "39.0.1"
27
+ pyvirtualdisplay = "^3.0"
28
+ numexpr = "^2.8.4"
29
+ gym3 = "^0.3.3"
30
+ glfw = "1.12.0"
31
+ ipython = "^8.10.0"
32
+
33
+ [build-system]
34
+ requires = ["poetry-core"]
35
+ build-backend = "poetry.core.masonry.api"
replay.meta.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\\nbuilt with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\\nconfiguration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\\nlibavutil 56. 31.100 / 56. 31.100\\nlibavcodec 58. 54.100 / 58. 54.100\\nlibavformat 58. 29.100 / 58. 29.100\\nlibavdevice 58. 8.100 / 58. 8.100\\nlibavfilter 7. 57.100 / 7. 57.100\\nlibavresample 4. 0. 0 / 4. 0. 0\\nlibswscale 5. 5.100 / 5. 5.100\\nlibswresample 3. 5.100 / 3. 5.100\\nlibpostproc 55. 5.100 / 55. 5.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "160x210", "-pix_fmt", "rgb24", "-framerate", "30", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "30", "/tmp/tmp5g9x6mzt/a2c-PongNoFrameskip-v4/replay.mp4"]}, "episode": {"r": 21.0, "l": 6634, "t": 3.800911}}
replay.mp4 ADDED
Binary file (115 kB). View file
 
runner/config.py ADDED
@@ -0,0 +1,155 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ from datetime import datetime
4
+ from dataclasses import dataclass
5
+ from typing import Any, Dict, NamedTuple, Optional, TypedDict, Union
6
+
7
+
8
+ @dataclass
9
+ class RunArgs:
10
+ algo: str
11
+ env: str
12
+ seed: Optional[int] = None
13
+ use_deterministic_algorithms: bool = True
14
+
15
+
16
+ class EnvHyperparams(NamedTuple):
17
+ env_type: str = "gymvec"
18
+ n_envs: int = 1
19
+ frame_stack: int = 1
20
+ make_kwargs: Optional[Dict[str, Any]] = None
21
+ no_reward_timeout_steps: Optional[int] = None
22
+ no_reward_fire_steps: Optional[int] = None
23
+ vec_env_class: str = "sync"
24
+ normalize: bool = False
25
+ normalize_kwargs: Optional[Dict[str, Any]] = None
26
+ rolling_length: int = 100
27
+ train_record_video: bool = False
28
+ video_step_interval: Union[int, float] = 1_000_000
29
+ initial_steps_to_truncate: Optional[int] = None
30
+ clip_atari_rewards: bool = True
31
+
32
+
33
+ class Hyperparams(TypedDict, total=False):
34
+ device: str
35
+ n_timesteps: Union[int, float]
36
+ env_hyperparams: Dict[str, Any]
37
+ policy_hyperparams: Dict[str, Any]
38
+ algo_hyperparams: Dict[str, Any]
39
+ eval_params: Dict[str, Any]
40
+
41
+
42
+ @dataclass
43
+ class Config:
44
+ args: RunArgs
45
+ hyperparams: Hyperparams
46
+ root_dir: str
47
+ run_id: str = datetime.now().isoformat()
48
+
49
+ def seed(self, training: bool = True) -> Optional[int]:
50
+ seed = self.args.seed
51
+ if training or seed is None:
52
+ return seed
53
+ return seed + self.env_hyperparams.get("n_envs", 1)
54
+
55
+ @property
56
+ def device(self) -> str:
57
+ return self.hyperparams.get("device", "auto")
58
+
59
+ @property
60
+ def n_timesteps(self) -> int:
61
+ return int(self.hyperparams.get("n_timesteps", 100_000))
62
+
63
+ @property
64
+ def env_hyperparams(self) -> Dict[str, Any]:
65
+ return self.hyperparams.get("env_hyperparams", {})
66
+
67
+ @property
68
+ def policy_hyperparams(self) -> Dict[str, Any]:
69
+ return self.hyperparams.get("policy_hyperparams", {})
70
+
71
+ @property
72
+ def algo_hyperparams(self) -> Dict[str, Any]:
73
+ return self.hyperparams.get("algo_hyperparams", {})
74
+
75
+ @property
76
+ def eval_params(self) -> Dict[str, Any]:
77
+ return self.hyperparams.get("eval_params", {})
78
+
79
+ @property
80
+ def algo(self) -> str:
81
+ return self.args.algo
82
+
83
+ @property
84
+ def env_id(self) -> str:
85
+ return self.hyperparams.get("env_id") or self.args.env
86
+
87
+ def model_name(self, include_seed: bool = True) -> str:
88
+ # Use arg env name instead of environment name
89
+ parts = [self.algo, self.args.env]
90
+ if include_seed and self.args.seed is not None:
91
+ parts.append(f"S{self.args.seed}")
92
+
93
+ # Assume that the custom arg name already has the necessary information
94
+ if not self.hyperparams.get("env_id"):
95
+ make_kwargs = self.env_hyperparams.get("make_kwargs", {})
96
+ if make_kwargs:
97
+ for k, v in make_kwargs.items():
98
+ if type(v) == bool and v:
99
+ parts.append(k)
100
+ elif type(v) == int and v:
101
+ parts.append(f"{k}{v}")
102
+ else:
103
+ parts.append(str(v))
104
+
105
+ return "-".join(parts)
106
+
107
+ @property
108
+ def run_name(self) -> str:
109
+ parts = [self.model_name(), self.run_id]
110
+ return "-".join(parts)
111
+
112
+ @property
113
+ def saved_models_dir(self) -> str:
114
+ return os.path.join(self.root_dir, "saved_models")
115
+
116
+ @property
117
+ def downloaded_models_dir(self) -> str:
118
+ return os.path.join(self.root_dir, "downloaded_models")
119
+
120
+ def model_dir_name(
121
+ self,
122
+ best: bool = False,
123
+ extension: str = "",
124
+ ) -> str:
125
+ return self.model_name() + ("-best" if best else "") + extension
126
+
127
+ def model_dir_path(self, best: bool = False, downloaded: bool = False) -> str:
128
+ return os.path.join(
129
+ self.saved_models_dir if not downloaded else self.downloaded_models_dir,
130
+ self.model_dir_name(best=best),
131
+ )
132
+
133
+ @property
134
+ def runs_dir(self) -> str:
135
+ return os.path.join(self.root_dir, "runs")
136
+
137
+ @property
138
+ def tensorboard_summary_path(self) -> str:
139
+ return os.path.join(self.runs_dir, self.run_name)
140
+
141
+ @property
142
+ def logs_path(self) -> str:
143
+ return os.path.join(self.runs_dir, f"log.yml")
144
+
145
+ @property
146
+ def videos_dir(self) -> str:
147
+ return os.path.join(self.root_dir, "videos")
148
+
149
+ @property
150
+ def video_prefix(self) -> str:
151
+ return os.path.join(self.videos_dir, self.model_name())
152
+
153
+ @property
154
+ def best_videos_dir(self) -> str:
155
+ return os.path.join(self.videos_dir, f"{self.model_name()}-best")
runner/env.py ADDED
@@ -0,0 +1,281 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gym
2
+ import numpy as np
3
+ import os
4
+
5
+ from gym.vector.async_vector_env import AsyncVectorEnv
6
+ from gym.vector.sync_vector_env import SyncVectorEnv
7
+ from gym.wrappers.resize_observation import ResizeObservation
8
+ from gym.wrappers.gray_scale_observation import GrayScaleObservation
9
+ from gym.wrappers.frame_stack import FrameStack
10
+ from stable_baselines3.common.atari_wrappers import (
11
+ MaxAndSkipEnv,
12
+ NoopResetEnv,
13
+ )
14
+ from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
15
+ from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv
16
+ from stable_baselines3.common.vec_env.vec_normalize import VecNormalize
17
+ from torch.utils.tensorboard.writer import SummaryWriter
18
+ from typing import Callable, Optional
19
+
20
+ from runner.config import Config, EnvHyperparams
21
+ from shared.policy.policy import VEC_NORMALIZE_FILENAME
22
+ from wrappers.atari_wrappers import EpisodicLifeEnv, FireOnLifeStarttEnv, ClipRewardEnv
23
+ from wrappers.episode_record_video import EpisodeRecordVideo
24
+ from wrappers.episode_stats_writer import EpisodeStatsWriter
25
+ from wrappers.initial_step_truncate_wrapper import InitialStepTruncateWrapper
26
+ from wrappers.is_vector_env import IsVectorEnv
27
+ from wrappers.noop_env_seed import NoopEnvSeed
28
+ from wrappers.normalize import NormalizeObservation, NormalizeReward
29
+ from wrappers.transpose_image_observation import TransposeImageObservation
30
+ from wrappers.vectorable_wrapper import VecEnv
31
+ from wrappers.video_compat_wrapper import VideoCompatWrapper
32
+
33
+
34
+ def make_env(
35
+ config: Config,
36
+ hparams: EnvHyperparams,
37
+ training: bool = True,
38
+ render: bool = False,
39
+ normalize_load_path: Optional[str] = None,
40
+ tb_writer: Optional[SummaryWriter] = None,
41
+ ) -> VecEnv:
42
+ if hparams.env_type == "procgen":
43
+ return _make_procgen_env(
44
+ config,
45
+ hparams,
46
+ training=training,
47
+ render=render,
48
+ normalize_load_path=normalize_load_path,
49
+ tb_writer=tb_writer,
50
+ )
51
+ elif hparams.env_type in {"sb3vec", "gymvec"}:
52
+ return _make_vec_env(
53
+ config,
54
+ hparams,
55
+ training=training,
56
+ render=render,
57
+ normalize_load_path=normalize_load_path,
58
+ tb_writer=tb_writer,
59
+ )
60
+ else:
61
+ raise ValueError(f"env_type {hparams.env_type} not supported")
62
+
63
+
64
+ def make_eval_env(
65
+ config: Config,
66
+ hparams: EnvHyperparams,
67
+ override_n_envs: Optional[int] = None,
68
+ **kwargs,
69
+ ) -> VecEnv:
70
+ kwargs = kwargs.copy()
71
+ kwargs["training"] = False
72
+ if override_n_envs is not None:
73
+ hparams_kwargs = hparams._asdict()
74
+ hparams_kwargs["n_envs"] = override_n_envs
75
+ if override_n_envs == 1:
76
+ hparams_kwargs["vec_env_class"] = "sync"
77
+ hparams = EnvHyperparams(**hparams_kwargs)
78
+ return make_env(config, hparams, **kwargs)
79
+
80
+
81
+ def _make_vec_env(
82
+ config: Config,
83
+ hparams: EnvHyperparams,
84
+ training: bool = True,
85
+ render: bool = False,
86
+ normalize_load_path: Optional[str] = None,
87
+ tb_writer: Optional[SummaryWriter] = None,
88
+ ) -> VecEnv:
89
+ (
90
+ env_type,
91
+ n_envs,
92
+ frame_stack,
93
+ make_kwargs,
94
+ no_reward_timeout_steps,
95
+ no_reward_fire_steps,
96
+ vec_env_class,
97
+ normalize,
98
+ normalize_kwargs,
99
+ rolling_length,
100
+ train_record_video,
101
+ video_step_interval,
102
+ initial_steps_to_truncate,
103
+ clip_atari_rewards,
104
+ ) = hparams
105
+
106
+ if "BulletEnv" in config.env_id:
107
+ import pybullet_envs
108
+
109
+ spec = gym.spec(config.env_id)
110
+ seed = config.seed(training=training)
111
+
112
+ make_kwargs = make_kwargs.copy() if make_kwargs is not None else {}
113
+ if "BulletEnv" in config.env_id and render:
114
+ make_kwargs["render"] = True
115
+ if "CarRacing" in config.env_id:
116
+ make_kwargs["verbose"] = 0
117
+ if "procgen" in config.env_id:
118
+ if not render:
119
+ make_kwargs["render_mode"] = "rgb_array"
120
+
121
+ def make(idx: int) -> Callable[[], gym.Env]:
122
+ def _make() -> gym.Env:
123
+ env = gym.make(config.env_id, **make_kwargs)
124
+ env = gym.wrappers.RecordEpisodeStatistics(env)
125
+ env = VideoCompatWrapper(env)
126
+ if training and train_record_video and idx == 0:
127
+ env = EpisodeRecordVideo(
128
+ env,
129
+ config.video_prefix,
130
+ step_increment=n_envs,
131
+ video_step_interval=int(video_step_interval),
132
+ )
133
+ if training and initial_steps_to_truncate:
134
+ env = InitialStepTruncateWrapper(
135
+ env, idx * initial_steps_to_truncate // n_envs
136
+ )
137
+ if "AtariEnv" in spec.entry_point: # type: ignore
138
+ env = NoopResetEnv(env, noop_max=30)
139
+ env = MaxAndSkipEnv(env, skip=4)
140
+ env = EpisodicLifeEnv(env, training=training)
141
+ action_meanings = env.unwrapped.get_action_meanings()
142
+ if "FIRE" in action_meanings: # type: ignore
143
+ env = FireOnLifeStarttEnv(env, action_meanings.index("FIRE"))
144
+ if clip_atari_rewards:
145
+ env = ClipRewardEnv(env, training=training)
146
+ env = ResizeObservation(env, (84, 84))
147
+ env = GrayScaleObservation(env, keep_dim=False)
148
+ env = FrameStack(env, frame_stack)
149
+ elif "CarRacing" in config.env_id:
150
+ env = ResizeObservation(env, (64, 64))
151
+ env = GrayScaleObservation(env, keep_dim=False)
152
+ env = FrameStack(env, frame_stack)
153
+ elif "procgen" in config.env_id:
154
+ # env = GrayScaleObservation(env, keep_dim=False)
155
+ env = NoopEnvSeed(env)
156
+ env = TransposeImageObservation(env)
157
+ if frame_stack > 1:
158
+ env = FrameStack(env, frame_stack)
159
+
160
+ if no_reward_timeout_steps:
161
+ from wrappers.no_reward_timeout import NoRewardTimeout
162
+
163
+ env = NoRewardTimeout(
164
+ env, no_reward_timeout_steps, n_fire_steps=no_reward_fire_steps
165
+ )
166
+
167
+ if seed is not None:
168
+ env.seed(seed + idx)
169
+ env.action_space.seed(seed + idx)
170
+ env.observation_space.seed(seed + idx)
171
+
172
+ return env
173
+
174
+ return _make
175
+
176
+ if env_type == "sb3vec":
177
+ VecEnvClass = {"sync": DummyVecEnv, "async": SubprocVecEnv}[vec_env_class]
178
+ elif env_type == "gymvec":
179
+ VecEnvClass = {"sync": SyncVectorEnv, "async": AsyncVectorEnv}[vec_env_class]
180
+ else:
181
+ raise ValueError(f"env_type {env_type} unsupported")
182
+ envs = VecEnvClass([make(i) for i in range(n_envs)])
183
+ if training:
184
+ assert tb_writer
185
+ envs = EpisodeStatsWriter(
186
+ envs, tb_writer, training=training, rolling_length=rolling_length
187
+ )
188
+ if normalize:
189
+ normalize_kwargs = normalize_kwargs or {}
190
+ if env_type == "sb3vec":
191
+ if normalize_load_path:
192
+ envs = VecNormalize.load(
193
+ os.path.join(normalize_load_path, VEC_NORMALIZE_FILENAME),
194
+ envs, # type: ignore
195
+ )
196
+ else:
197
+ envs = VecNormalize(
198
+ envs, # type: ignore
199
+ training=training,
200
+ **normalize_kwargs,
201
+ )
202
+ if not training:
203
+ envs.norm_reward = False
204
+ else:
205
+ if normalize_kwargs.get("norm_obs", True):
206
+ envs = NormalizeObservation(
207
+ envs, training=training, clip=normalize_kwargs.get("clip_obs", 10.0)
208
+ )
209
+ if training and normalize_kwargs.get("norm_reward", True):
210
+ envs = NormalizeReward(
211
+ envs,
212
+ training=training,
213
+ clip=normalize_kwargs.get("clip_reward", 10.0),
214
+ )
215
+ return envs
216
+
217
+
218
+ def _make_procgen_env(
219
+ config: Config,
220
+ hparams: EnvHyperparams,
221
+ training: bool = True,
222
+ render: bool = False,
223
+ normalize_load_path: Optional[str] = None,
224
+ tb_writer: Optional[SummaryWriter] = None,
225
+ ) -> VecEnv:
226
+ from gym3 import ViewerWrapper, ExtractDictObWrapper
227
+ from procgen.env import ProcgenGym3Env, ToBaselinesVecEnv
228
+
229
+ (
230
+ _,
231
+ n_envs,
232
+ frame_stack,
233
+ make_kwargs,
234
+ _, # no_reward_timeout_steps
235
+ _, # no_reward_fire_steps
236
+ _, # vec_env_class
237
+ normalize,
238
+ normalize_kwargs,
239
+ rolling_length,
240
+ _, # train_record_video
241
+ _, # video_step_interval
242
+ _, # initial_steps_to_truncate
243
+ _, # clip_atari_rewards
244
+ ) = hparams
245
+
246
+ seed = config.seed(training=training)
247
+
248
+ make_kwargs = make_kwargs or {}
249
+ make_kwargs["render_mode"] = "rgb_array"
250
+ if seed is not None:
251
+ make_kwargs["rand_seed"] = seed
252
+
253
+ envs = ProcgenGym3Env(n_envs, config.env_id, **make_kwargs)
254
+ envs = ExtractDictObWrapper(envs, key="rgb")
255
+ if render:
256
+ envs = ViewerWrapper(envs, info_key="rgb")
257
+ envs = ToBaselinesVecEnv(envs)
258
+ envs = IsVectorEnv(envs)
259
+ # TODO: Handle Grayscale and/or FrameStack
260
+ envs = TransposeImageObservation(envs)
261
+
262
+ envs = gym.wrappers.RecordEpisodeStatistics(envs)
263
+
264
+ if seed is not None:
265
+ envs.action_space.seed(seed)
266
+ envs.observation_space.seed(seed)
267
+
268
+ if training:
269
+ assert tb_writer
270
+ envs = EpisodeStatsWriter(
271
+ envs, tb_writer, training=training, rolling_length=rolling_length
272
+ )
273
+ if normalize and training:
274
+ normalize_kwargs = normalize_kwargs or {}
275
+ envs = gym.wrappers.NormalizeReward(envs)
276
+ clip_obs = normalize_kwargs.get("clip_reward", 10.0)
277
+ envs = gym.wrappers.TransformReward(
278
+ envs, lambda r: np.clip(r, -clip_obs, clip_obs)
279
+ )
280
+
281
+ return envs # type: ignore
runner/evaluate.py ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import shutil
3
+
4
+ from dataclasses import dataclass
5
+ from typing import NamedTuple, Optional
6
+
7
+ from runner.env import make_eval_env
8
+ from runner.config import Config, EnvHyperparams, RunArgs
9
+ from runner.running_utils import (
10
+ load_hyperparams,
11
+ set_seeds,
12
+ get_device,
13
+ make_policy,
14
+ )
15
+ from shared.callbacks.eval_callback import evaluate
16
+ from shared.policy.policy import Policy
17
+ from shared.stats import EpisodesStats
18
+
19
+
20
+ @dataclass
21
+ class EvalArgs(RunArgs):
22
+ render: bool = True
23
+ best: bool = True
24
+ n_envs: Optional[int] = 1
25
+ n_episodes: int = 3
26
+ deterministic_eval: Optional[bool] = None
27
+ no_print_returns: bool = False
28
+ wandb_run_path: Optional[str] = None
29
+
30
+
31
+ class Evaluation(NamedTuple):
32
+ policy: Policy
33
+ stats: EpisodesStats
34
+ config: Config
35
+
36
+
37
+ def evaluate_model(args: EvalArgs, root_dir: str) -> Evaluation:
38
+ if args.wandb_run_path:
39
+ import wandb
40
+
41
+ api = wandb.Api()
42
+ run = api.run(args.wandb_run_path)
43
+ hyperparams = run.config
44
+
45
+ args.algo = hyperparams["algo"]
46
+ args.env = hyperparams["env"]
47
+ args.seed = hyperparams.get("seed", None)
48
+ args.use_deterministic_algorithms = hyperparams.get(
49
+ "use_deterministic_algorithms", True
50
+ )
51
+
52
+ config = Config(args, hyperparams, root_dir)
53
+ model_path = config.model_dir_path(best=args.best, downloaded=True)
54
+
55
+ model_archive_name = config.model_dir_name(best=args.best, extension=".zip")
56
+ run.file(model_archive_name).download()
57
+ if os.path.isdir(model_path):
58
+ shutil.rmtree(model_path)
59
+ shutil.unpack_archive(model_archive_name, model_path)
60
+ os.remove(model_archive_name)
61
+ else:
62
+ hyperparams = load_hyperparams(args.algo, args.env, root_dir)
63
+
64
+ config = Config(args, hyperparams, root_dir)
65
+ model_path = config.model_dir_path(best=args.best)
66
+
67
+ print(args)
68
+
69
+ set_seeds(args.seed, args.use_deterministic_algorithms)
70
+
71
+ env = make_eval_env(
72
+ config,
73
+ EnvHyperparams(**config.env_hyperparams),
74
+ override_n_envs=args.n_envs,
75
+ render=args.render,
76
+ normalize_load_path=model_path,
77
+ )
78
+ device = get_device(config.device, env)
79
+ policy = make_policy(
80
+ args.algo,
81
+ env,
82
+ device,
83
+ load_path=model_path,
84
+ **config.policy_hyperparams,
85
+ ).eval()
86
+
87
+ deterministic = (
88
+ args.deterministic_eval
89
+ if args.deterministic_eval is not None
90
+ else config.eval_params.get("deterministic", True)
91
+ )
92
+ return Evaluation(
93
+ policy,
94
+ evaluate(
95
+ env,
96
+ policy,
97
+ args.n_episodes,
98
+ render=args.render,
99
+ deterministic=deterministic,
100
+ print_returns=not args.no_print_returns,
101
+ ),
102
+ config,
103
+ )
runner/running_utils.py ADDED
@@ -0,0 +1,195 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import gym
3
+ import json
4
+ import matplotlib.pyplot as plt
5
+ import numpy as np
6
+ import os
7
+ import random
8
+ import torch
9
+ import torch.backends.cudnn
10
+ import yaml
11
+
12
+ from gym.spaces import Box, Discrete
13
+ from torch.utils.tensorboard.writer import SummaryWriter
14
+ from typing import Dict, Optional, Type, Union
15
+
16
+ from runner.config import Hyperparams
17
+ from shared.algorithm import Algorithm
18
+ from shared.callbacks.eval_callback import EvalCallback
19
+ from shared.policy.on_policy import ActorCritic
20
+ from shared.policy.policy import Policy
21
+
22
+ from a2c.a2c import A2C
23
+ from dqn.dqn import DQN
24
+ from dqn.policy import DQNPolicy
25
+ from ppo.ppo import PPO
26
+ from vpg.vpg import VanillaPolicyGradient
27
+ from vpg.policy import VPGActorCritic
28
+ from wrappers.vectorable_wrapper import VecEnv, single_observation_space
29
+
30
+ ALGOS: Dict[str, Type[Algorithm]] = {
31
+ "dqn": DQN,
32
+ "vpg": VanillaPolicyGradient,
33
+ "ppo": PPO,
34
+ "a2c": A2C,
35
+ }
36
+ POLICIES: Dict[str, Type[Policy]] = {
37
+ "dqn": DQNPolicy,
38
+ "vpg": VPGActorCritic,
39
+ "ppo": ActorCritic,
40
+ "a2c": ActorCritic,
41
+ }
42
+
43
+ HYPERPARAMS_PATH = "hyperparams"
44
+
45
+
46
+ def base_parser(multiple: bool = True) -> argparse.ArgumentParser:
47
+ parser = argparse.ArgumentParser()
48
+ parser.add_argument(
49
+ "--algo",
50
+ default=["dqn"],
51
+ type=str,
52
+ choices=list(ALGOS.keys()),
53
+ nargs="+" if multiple else 1,
54
+ help="Abbreviation(s) of algorithm(s)",
55
+ )
56
+ parser.add_argument(
57
+ "--env",
58
+ default=["CartPole-v1"],
59
+ type=str,
60
+ nargs="+" if multiple else 1,
61
+ help="Name of environment(s) in gym",
62
+ )
63
+ parser.add_argument(
64
+ "--seed",
65
+ default=[1],
66
+ type=int,
67
+ nargs="*" if multiple else "?",
68
+ help="Seeds to run experiment. Unset will do one run with no set seed",
69
+ )
70
+ parser.add_argument(
71
+ "--use-deterministic-algorithms",
72
+ default=True,
73
+ type=bool,
74
+ help="If seed set, set torch.use_deterministic_algorithms",
75
+ )
76
+ return parser
77
+
78
+
79
+ def load_hyperparams(algo: str, env_id: str, root_path: str) -> Hyperparams:
80
+ hyperparams_path = os.path.join(root_path, HYPERPARAMS_PATH, f"{algo}.yml")
81
+ with open(hyperparams_path, "r") as f:
82
+ hyperparams_dict = yaml.safe_load(f)
83
+
84
+ if env_id in hyperparams_dict:
85
+ return hyperparams_dict[env_id]
86
+
87
+ if "BulletEnv" in env_id:
88
+ import pybullet_envs
89
+ spec = gym.spec(env_id)
90
+ if "AtariEnv" in str(spec.entry_point) and "_atari" in hyperparams_dict:
91
+ return hyperparams_dict["_atari"]
92
+ else:
93
+ raise ValueError(f"{env_id} not specified in {algo} hyperparameters file")
94
+
95
+
96
+ def get_device(device: str, env: VecEnv) -> torch.device:
97
+ # cuda by default
98
+ if device == "auto":
99
+ device = "cuda"
100
+ # Apple MPS is a second choice (sometimes)
101
+ if device == "cuda" and not torch.cuda.is_available():
102
+ device = "mps"
103
+ # If no MPS, fallback to cpu
104
+ if device == "mps" and not torch.backends.mps.is_available():
105
+ device = "cpu"
106
+ # Simple environments like Discreet and 1-D Boxes might also be better
107
+ # served with the CPU.
108
+ if device == "mps":
109
+ obs_space = single_observation_space(env)
110
+ if isinstance(obs_space, Discrete):
111
+ device = "cpu"
112
+ elif isinstance(obs_space, Box) and len(obs_space.shape) == 1:
113
+ device = "cpu"
114
+ print(f"Device: {device}")
115
+ return torch.device(device)
116
+
117
+
118
+ def set_seeds(seed: Optional[int], use_deterministic_algorithms: bool) -> None:
119
+ if seed is None:
120
+ return
121
+ random.seed(seed)
122
+ np.random.seed(seed)
123
+ torch.manual_seed(seed)
124
+ torch.backends.cudnn.benchmark = False
125
+ torch.use_deterministic_algorithms(use_deterministic_algorithms)
126
+ os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
127
+ # Stop warning and it would introduce stochasticity if I was using TF
128
+ os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
129
+
130
+
131
+ def make_policy(
132
+ algo: str,
133
+ env: VecEnv,
134
+ device: torch.device,
135
+ load_path: Optional[str] = None,
136
+ **kwargs,
137
+ ) -> Policy:
138
+ policy = POLICIES[algo](env, **kwargs).to(device)
139
+ if load_path:
140
+ policy.load(load_path)
141
+ return policy
142
+
143
+
144
+ def plot_eval_callback(callback: EvalCallback, tb_writer: SummaryWriter, run_name: str):
145
+ figure = plt.figure()
146
+ cumulative_steps = [
147
+ (idx + 1) * callback.step_freq for idx in range(len(callback.stats))
148
+ ]
149
+ plt.plot(
150
+ cumulative_steps,
151
+ [s.score.mean for s in callback.stats],
152
+ "b-",
153
+ label="mean",
154
+ )
155
+ plt.plot(
156
+ cumulative_steps,
157
+ [s.score.mean - s.score.std for s in callback.stats],
158
+ "g--",
159
+ label="mean-std",
160
+ )
161
+ plt.fill_between(
162
+ cumulative_steps,
163
+ [s.score.min for s in callback.stats], # type: ignore
164
+ [s.score.max for s in callback.stats], # type: ignore
165
+ facecolor="cyan",
166
+ label="range",
167
+ )
168
+ plt.xlabel("Steps")
169
+ plt.ylabel("Score")
170
+ plt.legend()
171
+ plt.title(f"Eval {run_name}")
172
+ tb_writer.add_figure("eval", figure)
173
+
174
+
175
+ Scalar = Union[bool, str, float, int, None]
176
+
177
+
178
+ def hparam_dict(
179
+ hyperparams: Hyperparams, args: Dict[str, Union[Scalar, list]]
180
+ ) -> Dict[str, Scalar]:
181
+ flattened = args.copy()
182
+ for k, v in flattened.items():
183
+ if isinstance(v, list):
184
+ flattened[k] = json.dumps(v)
185
+ for k, v in hyperparams.items():
186
+ if isinstance(v, dict):
187
+ for sk, sv in v.items():
188
+ key = f"{k}/{sk}"
189
+ if isinstance(sv, dict) or isinstance(sv, list):
190
+ flattened[key] = str(sv)
191
+ else:
192
+ flattened[key] = sv
193
+ else:
194
+ flattened[k] = v # type: ignore
195
+ return flattened # type: ignore
runner/train.py ADDED
@@ -0,0 +1,141 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Support for PyTorch mps mode (https://pytorch.org/docs/stable/notes/mps.html)
2
+ import os
3
+
4
+ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
5
+
6
+ import dataclasses
7
+ import shutil
8
+ import wandb
9
+ import yaml
10
+
11
+ from dataclasses import dataclass
12
+ from torch.utils.tensorboard.writer import SummaryWriter
13
+ from typing import Any, Dict, Optional, Sequence
14
+
15
+ from shared.callbacks.eval_callback import EvalCallback
16
+ from runner.config import Config, EnvHyperparams, RunArgs
17
+ from runner.env import make_env, make_eval_env
18
+ from runner.running_utils import (
19
+ ALGOS,
20
+ load_hyperparams,
21
+ set_seeds,
22
+ get_device,
23
+ make_policy,
24
+ plot_eval_callback,
25
+ hparam_dict,
26
+ )
27
+ from shared.stats import EpisodesStats
28
+
29
+
30
+ @dataclass
31
+ class TrainArgs(RunArgs):
32
+ wandb_project_name: Optional[str] = None
33
+ wandb_entity: Optional[str] = None
34
+ wandb_tags: Sequence[str] = dataclasses.field(default_factory=list)
35
+
36
+
37
+ def train(args: TrainArgs):
38
+ print(args)
39
+ hyperparams = load_hyperparams(args.algo, args.env, os.getcwd())
40
+ print(hyperparams)
41
+ config = Config(args, hyperparams, os.getcwd())
42
+
43
+ wandb_enabled = args.wandb_project_name
44
+ if wandb_enabled:
45
+ wandb.tensorboard.patch(
46
+ root_logdir=config.tensorboard_summary_path, pytorch=True
47
+ )
48
+ wandb.init(
49
+ project=args.wandb_project_name,
50
+ entity=args.wandb_entity,
51
+ config=hyperparams, # type: ignore
52
+ name=config.run_name,
53
+ monitor_gym=True,
54
+ save_code=True,
55
+ tags=args.wandb_tags,
56
+ )
57
+ wandb.config.update(args)
58
+
59
+ tb_writer = SummaryWriter(config.tensorboard_summary_path)
60
+
61
+ set_seeds(args.seed, args.use_deterministic_algorithms)
62
+
63
+ env = make_env(
64
+ config, EnvHyperparams(**config.env_hyperparams), tb_writer=tb_writer
65
+ )
66
+ device = get_device(config.device, env)
67
+ policy = make_policy(args.algo, env, device, **config.policy_hyperparams)
68
+ algo = ALGOS[args.algo](policy, env, device, tb_writer, **config.algo_hyperparams)
69
+
70
+ num_parameters = policy.num_parameters()
71
+ num_trainable_parameters = policy.num_trainable_parameters()
72
+ if wandb_enabled:
73
+ wandb.run.summary["num_parameters"] = num_parameters
74
+ wandb.run.summary["num_trainable_parameters"] = num_trainable_parameters
75
+ else:
76
+ print(
77
+ f"num_parameters = {num_parameters} ; "
78
+ f"num_trainable_parameters = {num_trainable_parameters}"
79
+ )
80
+
81
+ eval_env = make_eval_env(config, EnvHyperparams(**config.env_hyperparams))
82
+ record_best_videos = config.eval_params.get("record_best_videos", True)
83
+ callback = EvalCallback(
84
+ policy,
85
+ eval_env,
86
+ tb_writer,
87
+ best_model_path=config.model_dir_path(best=True),
88
+ **config.eval_params,
89
+ video_env=make_eval_env(
90
+ config, EnvHyperparams(**config.env_hyperparams), override_n_envs=1
91
+ )
92
+ if record_best_videos
93
+ else None,
94
+ best_video_dir=config.best_videos_dir,
95
+ )
96
+ algo.learn(config.n_timesteps, callback=callback)
97
+
98
+ policy.save(config.model_dir_path(best=False))
99
+
100
+ eval_stats = callback.evaluate(n_episodes=10, print_returns=True)
101
+
102
+ plot_eval_callback(callback, tb_writer, config.run_name)
103
+
104
+ log_dict: Dict[str, Any] = {
105
+ "eval": eval_stats._asdict(),
106
+ }
107
+ if callback.best:
108
+ log_dict["best_eval"] = callback.best._asdict()
109
+ log_dict.update(hyperparams)
110
+ log_dict.update(vars(args))
111
+ with open(config.logs_path, "a") as f:
112
+ yaml.dump({config.run_name: log_dict}, f)
113
+
114
+ best_eval_stats: EpisodesStats = callback.best # type: ignore
115
+ tb_writer.add_hparams(
116
+ hparam_dict(hyperparams, vars(args)),
117
+ {
118
+ "hparam/best_mean": best_eval_stats.score.mean,
119
+ "hparam/best_result": best_eval_stats.score.mean
120
+ - best_eval_stats.score.std,
121
+ "hparam/last_mean": eval_stats.score.mean,
122
+ "hparam/last_result": eval_stats.score.mean - eval_stats.score.std,
123
+ },
124
+ None,
125
+ config.run_name,
126
+ )
127
+
128
+ tb_writer.close()
129
+
130
+ if wandb_enabled:
131
+ shutil.make_archive(
132
+ os.path.join(wandb.run.dir, config.model_dir_name()),
133
+ "zip",
134
+ config.model_dir_path(),
135
+ )
136
+ shutil.make_archive(
137
+ os.path.join(wandb.run.dir, config.model_dir_name(best=True)),
138
+ "zip",
139
+ config.model_dir_path(best=True),
140
+ )
141
+ wandb.finish()
saved_models/a2c-PongNoFrameskip-v4-S3-best/model.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6cf582760ef73a22097e5c9699d683b469f9fbe43983a52cbd2dd415dffe9522
3
+ size 6755173
shared/algorithm.py ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gym
2
+ import torch
3
+
4
+ from abc import ABC, abstractmethod
5
+ from torch.utils.tensorboard.writer import SummaryWriter
6
+ from typing import List, Optional, TypeVar
7
+
8
+ from shared.callbacks.callback import Callback
9
+ from shared.policy.policy import Policy
10
+ from wrappers.vectorable_wrapper import VecEnv
11
+
12
+ AlgorithmSelf = TypeVar("AlgorithmSelf", bound="Algorithm")
13
+
14
+
15
+ class Algorithm(ABC):
16
+ @abstractmethod
17
+ def __init__(
18
+ self,
19
+ policy: Policy,
20
+ env: VecEnv,
21
+ device: torch.device,
22
+ tb_writer: SummaryWriter,
23
+ **kwargs,
24
+ ) -> None:
25
+ super().__init__()
26
+ self.policy = policy
27
+ self.env = env
28
+ self.device = device
29
+ self.tb_writer = tb_writer
30
+
31
+ @abstractmethod
32
+ def learn(
33
+ self: AlgorithmSelf, total_timesteps: int, callback: Optional[Callback] = None
34
+ ) -> AlgorithmSelf:
35
+ ...
shared/callbacks/callback.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from abc import ABC, abstractmethod
2
+
3
+
4
+ class Callback(ABC):
5
+
6
+ def __init__(self) -> None:
7
+ super().__init__()
8
+ self.timesteps_elapsed = 0
9
+
10
+ def on_step(self, timesteps_elapsed: int = 1) -> bool:
11
+ self.timesteps_elapsed += timesteps_elapsed
12
+ return True
shared/callbacks/eval_callback.py ADDED
@@ -0,0 +1,199 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import itertools
2
+ import numpy as np
3
+ import os
4
+
5
+ from time import perf_counter
6
+ from torch.utils.tensorboard.writer import SummaryWriter
7
+ from typing import List, Optional, Union
8
+
9
+ from shared.callbacks.callback import Callback
10
+ from shared.policy.policy import Policy
11
+ from shared.stats import Episode, EpisodeAccumulator, EpisodesStats
12
+ from wrappers.vec_episode_recorder import VecEpisodeRecorder
13
+ from wrappers.vectorable_wrapper import VecEnv
14
+
15
+
16
+ class EvaluateAccumulator(EpisodeAccumulator):
17
+ def __init__(
18
+ self,
19
+ num_envs: int,
20
+ goal_episodes: int,
21
+ print_returns: bool = True,
22
+ ignore_first_episode: bool = False,
23
+ ):
24
+ super().__init__(num_envs)
25
+ self.completed_episodes_by_env_idx = [[] for _ in range(num_envs)]
26
+ self.goal_episodes_per_env = int(np.ceil(goal_episodes / num_envs))
27
+ self.print_returns = print_returns
28
+ if ignore_first_episode:
29
+ first_done = set()
30
+
31
+ def should_record_done(idx: int) -> bool:
32
+ has_done_first_episode = idx in first_done
33
+ first_done.add(idx)
34
+ return has_done_first_episode
35
+
36
+ self.should_record_done = should_record_done
37
+ else:
38
+ self.should_record_done = lambda idx: True
39
+
40
+ def on_done(self, ep_idx: int, episode: Episode) -> None:
41
+ if (
42
+ self.should_record_done(ep_idx)
43
+ and len(self.completed_episodes_by_env_idx[ep_idx])
44
+ >= self.goal_episodes_per_env
45
+ ):
46
+ return
47
+ self.completed_episodes_by_env_idx[ep_idx].append(episode)
48
+ if self.print_returns:
49
+ print(
50
+ f"Episode {len(self)} | "
51
+ f"Score {episode.score} | "
52
+ f"Length {episode.length}"
53
+ )
54
+
55
+ def __len__(self) -> int:
56
+ return sum(len(ce) for ce in self.completed_episodes_by_env_idx)
57
+
58
+ @property
59
+ def episodes(self) -> List[Episode]:
60
+ return list(itertools.chain(*self.completed_episodes_by_env_idx))
61
+
62
+ def is_done(self) -> bool:
63
+ return all(
64
+ len(ce) == self.goal_episodes_per_env
65
+ for ce in self.completed_episodes_by_env_idx
66
+ )
67
+
68
+
69
+ def evaluate(
70
+ env: VecEnv,
71
+ policy: Policy,
72
+ n_episodes: int,
73
+ render: bool = False,
74
+ deterministic: bool = True,
75
+ print_returns: bool = True,
76
+ ignore_first_episode: bool = False,
77
+ ) -> EpisodesStats:
78
+ policy.eval()
79
+ episodes = EvaluateAccumulator(
80
+ env.num_envs, n_episodes, print_returns, ignore_first_episode
81
+ )
82
+
83
+ obs = env.reset()
84
+ while not episodes.is_done():
85
+ act = policy.act(obs, deterministic=deterministic)
86
+ obs, rew, done, _ = env.step(act)
87
+ episodes.step(rew, done)
88
+ if render:
89
+ env.render()
90
+ stats = EpisodesStats(episodes.episodes)
91
+ if print_returns:
92
+ print(stats)
93
+ return stats
94
+
95
+
96
+ class EvalCallback(Callback):
97
+ def __init__(
98
+ self,
99
+ policy: Policy,
100
+ env: VecEnv,
101
+ tb_writer: SummaryWriter,
102
+ best_model_path: Optional[str] = None,
103
+ step_freq: Union[int, float] = 50_000,
104
+ n_episodes: int = 10,
105
+ save_best: bool = True,
106
+ deterministic: bool = True,
107
+ record_best_videos: bool = True,
108
+ video_env: Optional[VecEnv] = None,
109
+ best_video_dir: Optional[str] = None,
110
+ max_video_length: int = 3600,
111
+ ignore_first_episode: bool = False,
112
+ ) -> None:
113
+ super().__init__()
114
+ self.policy = policy
115
+ self.env = env
116
+ self.tb_writer = tb_writer
117
+ self.best_model_path = best_model_path
118
+ self.step_freq = int(step_freq)
119
+ self.n_episodes = n_episodes
120
+ self.save_best = save_best
121
+ self.deterministic = deterministic
122
+ self.stats: List[EpisodesStats] = []
123
+ self.best = None
124
+
125
+ self.record_best_videos = record_best_videos
126
+ assert video_env or not record_best_videos
127
+ self.video_env = video_env
128
+ assert best_video_dir or not record_best_videos
129
+ self.best_video_dir = best_video_dir
130
+ if best_video_dir:
131
+ os.makedirs(best_video_dir, exist_ok=True)
132
+ self.max_video_length = max_video_length
133
+ self.best_video_base_path = None
134
+
135
+ self.ignore_first_episode = ignore_first_episode
136
+
137
+ def on_step(self, timesteps_elapsed: int = 1) -> bool:
138
+ super().on_step(timesteps_elapsed)
139
+ if self.timesteps_elapsed // self.step_freq >= len(self.stats):
140
+ self.policy.sync_normalization(self.env)
141
+ self.evaluate()
142
+ return True
143
+
144
+ def evaluate(
145
+ self, n_episodes: Optional[int] = None, print_returns: Optional[bool] = None
146
+ ) -> EpisodesStats:
147
+ start_time = perf_counter()
148
+ eval_stat = evaluate(
149
+ self.env,
150
+ self.policy,
151
+ n_episodes or self.n_episodes,
152
+ deterministic=self.deterministic,
153
+ print_returns=print_returns or False,
154
+ ignore_first_episode=self.ignore_first_episode,
155
+ )
156
+ end_time = perf_counter()
157
+ self.tb_writer.add_scalar(
158
+ "eval/steps_per_second",
159
+ eval_stat.length.sum() / (end_time - start_time),
160
+ self.timesteps_elapsed,
161
+ )
162
+ self.policy.train(True)
163
+ print(f"Eval Timesteps: {self.timesteps_elapsed} | {eval_stat}")
164
+
165
+ self.stats.append(eval_stat)
166
+
167
+ if not self.best or eval_stat >= self.best:
168
+ strictly_better = not self.best or eval_stat > self.best
169
+ self.best = eval_stat
170
+ if self.save_best:
171
+ assert self.best_model_path
172
+ self.policy.save(self.best_model_path)
173
+ print("Saved best model")
174
+ self.best.write_to_tensorboard(
175
+ self.tb_writer, "best_eval", self.timesteps_elapsed
176
+ )
177
+ if strictly_better and self.record_best_videos:
178
+ assert self.video_env and self.best_video_dir
179
+ self.policy.sync_normalization(self.video_env)
180
+ self.best_video_base_path = os.path.join(
181
+ self.best_video_dir, str(self.timesteps_elapsed)
182
+ )
183
+ video_wrapped = VecEpisodeRecorder(
184
+ self.video_env,
185
+ self.best_video_base_path,
186
+ max_video_length=self.max_video_length,
187
+ )
188
+ video_stats = evaluate(
189
+ video_wrapped,
190
+ self.policy,
191
+ 1,
192
+ deterministic=self.deterministic,
193
+ print_returns=False,
194
+ )
195
+ print(f"Saved best video: {video_stats}")
196
+
197
+ eval_stat.write_to_tensorboard(self.tb_writer, "eval", self.timesteps_elapsed)
198
+
199
+ return eval_stat
shared/gae.py ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import torch
3
+
4
+ from typing import NamedTuple, Sequence
5
+
6
+ from shared.policy.on_policy import OnPolicy
7
+ from shared.trajectory import Trajectory
8
+
9
+
10
+ class RtgAdvantage(NamedTuple):
11
+ rewards_to_go: torch.Tensor
12
+ advantage: torch.Tensor
13
+
14
+
15
+ def discounted_cumsum(x: np.ndarray, gamma: float) -> np.ndarray:
16
+ dc = x.copy()
17
+ for i in reversed(range(len(x) - 1)):
18
+ dc[i] += gamma * dc[i + 1]
19
+ return dc
20
+
21
+
22
+ def compute_advantage(
23
+ trajectories: Sequence[Trajectory],
24
+ policy: OnPolicy,
25
+ gamma: float,
26
+ gae_lambda: float,
27
+ device: torch.device,
28
+ ) -> torch.Tensor:
29
+ advantage = []
30
+ for traj in trajectories:
31
+ last_val = 0
32
+ if not traj.terminated and traj.next_obs is not None:
33
+ last_val = policy.value(traj.next_obs)
34
+ rew = np.append(np.array(traj.rew), last_val)
35
+ v = np.append(np.array(traj.v), last_val)
36
+ deltas = rew[:-1] + gamma * v[1:] - v[:-1]
37
+ advantage.append(discounted_cumsum(deltas, gamma * gae_lambda))
38
+ return torch.as_tensor(
39
+ np.concatenate(advantage), dtype=torch.float32, device=device
40
+ )
41
+
42
+
43
+ def compute_rtg_and_advantage(
44
+ trajectories: Sequence[Trajectory],
45
+ policy: OnPolicy,
46
+ gamma: float,
47
+ gae_lambda: float,
48
+ device: torch.device,
49
+ ) -> RtgAdvantage:
50
+ rewards_to_go = []
51
+ advantages = []
52
+ for traj in trajectories:
53
+ last_val = 0
54
+ if not traj.terminated and traj.next_obs is not None:
55
+ last_val = policy.value(traj.next_obs)
56
+ rew = np.append(np.array(traj.rew), last_val)
57
+ v = np.append(np.array(traj.v), last_val)
58
+ deltas = rew[:-1] + gamma * v[1:] - v[:-1]
59
+ adv = discounted_cumsum(deltas, gamma * gae_lambda)
60
+ advantages.append(adv)
61
+ rewards_to_go.append(v[:-1] + adv)
62
+ return RtgAdvantage(
63
+ torch.as_tensor(
64
+ np.concatenate(rewards_to_go), dtype=torch.float32, device=device
65
+ ),
66
+ torch.as_tensor(np.concatenate(advantages), dtype=torch.float32, device=device),
67
+ )
shared/module/feature_extractor.py ADDED
@@ -0,0 +1,215 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gym
2
+ import torch
3
+ import torch.nn as nn
4
+ import torch.nn.functional as F
5
+
6
+ from abc import ABC, abstractmethod
7
+ from gym.spaces import Box, Discrete
8
+ from stable_baselines3.common.preprocessing import get_flattened_obs_dim
9
+ from typing import Dict, Optional, Sequence, Type
10
+
11
+ from shared.module.module import layer_init
12
+
13
+
14
+ class CnnFeatureExtractor(nn.Module, ABC):
15
+ @abstractmethod
16
+ def __init__(
17
+ self,
18
+ in_channels: int,
19
+ activation: Type[nn.Module] = nn.ReLU,
20
+ init_layers_orthogonal: Optional[bool] = None,
21
+ **kwargs,
22
+ ) -> None:
23
+ super().__init__()
24
+
25
+
26
+ class NatureCnn(CnnFeatureExtractor):
27
+ """
28
+ CNN from DQN Nature paper: Mnih, Volodymyr, et al.
29
+ "Human-level control through deep reinforcement learning."
30
+ Nature 518.7540 (2015): 529-533.
31
+ """
32
+
33
+ def __init__(
34
+ self,
35
+ in_channels: int,
36
+ activation: Type[nn.Module] = nn.ReLU,
37
+ init_layers_orthogonal: Optional[bool] = None,
38
+ **kwargs,
39
+ ) -> None:
40
+ if init_layers_orthogonal is None:
41
+ init_layers_orthogonal = True
42
+ super().__init__(in_channels, activation, init_layers_orthogonal)
43
+ self.cnn = nn.Sequential(
44
+ layer_init(
45
+ nn.Conv2d(in_channels, 32, kernel_size=8, stride=4),
46
+ init_layers_orthogonal,
47
+ ),
48
+ activation(),
49
+ layer_init(
50
+ nn.Conv2d(32, 64, kernel_size=4, stride=2),
51
+ init_layers_orthogonal,
52
+ ),
53
+ activation(),
54
+ layer_init(
55
+ nn.Conv2d(64, 64, kernel_size=3, stride=1),
56
+ init_layers_orthogonal,
57
+ ),
58
+ activation(),
59
+ nn.Flatten(),
60
+ )
61
+
62
+ def forward(self, obs: torch.Tensor) -> torch.Tensor:
63
+ return self.cnn(obs)
64
+
65
+
66
+ class ResidualBlock(nn.Module):
67
+ def __init__(
68
+ self,
69
+ channels: int,
70
+ activation: Type[nn.Module] = nn.ReLU,
71
+ init_layers_orthogonal: bool = False,
72
+ ) -> None:
73
+ super().__init__()
74
+ self.residual = nn.Sequential(
75
+ activation(),
76
+ layer_init(
77
+ nn.Conv2d(channels, channels, 3, padding=1), init_layers_orthogonal
78
+ ),
79
+ activation(),
80
+ layer_init(
81
+ nn.Conv2d(channels, channels, 3, padding=1), init_layers_orthogonal
82
+ ),
83
+ )
84
+
85
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
86
+ return x + self.residual(x)
87
+
88
+
89
+ class ConvSequence(nn.Module):
90
+ def __init__(
91
+ self,
92
+ in_channels: int,
93
+ out_channels: int,
94
+ activation: Type[nn.Module] = nn.ReLU,
95
+ init_layers_orthogonal: bool = False,
96
+ ) -> None:
97
+ super().__init__()
98
+ self.seq = nn.Sequential(
99
+ layer_init(
100
+ nn.Conv2d(in_channels, out_channels, 3, padding=1),
101
+ init_layers_orthogonal,
102
+ ),
103
+ nn.MaxPool2d(3, stride=2, padding=1),
104
+ ResidualBlock(out_channels, activation, init_layers_orthogonal),
105
+ ResidualBlock(out_channels, activation, init_layers_orthogonal),
106
+ )
107
+
108
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
109
+ return self.seq(x)
110
+
111
+
112
+ class ImpalaCnn(CnnFeatureExtractor):
113
+ """
114
+ IMPALA-style CNN architecture
115
+ """
116
+
117
+ def __init__(
118
+ self,
119
+ in_channels: int,
120
+ activation: Type[nn.Module] = nn.ReLU,
121
+ init_layers_orthogonal: Optional[bool] = None,
122
+ impala_channels: Sequence[int] = (16, 32, 32),
123
+ **kwargs,
124
+ ) -> None:
125
+ if init_layers_orthogonal is None:
126
+ init_layers_orthogonal = False
127
+ super().__init__(in_channels, activation, init_layers_orthogonal)
128
+ sequences = []
129
+ for out_channels in impala_channels:
130
+ sequences.append(
131
+ ConvSequence(
132
+ in_channels, out_channels, activation, init_layers_orthogonal
133
+ )
134
+ )
135
+ in_channels = out_channels
136
+ sequences.extend(
137
+ [
138
+ activation(),
139
+ nn.Flatten(),
140
+ ]
141
+ )
142
+ self.seq = nn.Sequential(*sequences)
143
+
144
+ def forward(self, obs: torch.Tensor) -> torch.Tensor:
145
+ return self.seq(obs)
146
+
147
+
148
+ CNN_EXTRACTORS_BY_STYLE: Dict[str, Type[CnnFeatureExtractor]] = {
149
+ "nature": NatureCnn,
150
+ "impala": ImpalaCnn,
151
+ }
152
+
153
+
154
+ class FeatureExtractor(nn.Module):
155
+ def __init__(
156
+ self,
157
+ obs_space: gym.Space,
158
+ activation: Type[nn.Module],
159
+ init_layers_orthogonal: bool = False,
160
+ cnn_feature_dim: int = 512,
161
+ cnn_style: str = "nature",
162
+ cnn_layers_init_orthogonal: Optional[bool] = None,
163
+ impala_channels: Sequence[int] = (16, 32, 32),
164
+ ) -> None:
165
+ super().__init__()
166
+ if isinstance(obs_space, Box):
167
+ # Conv2D: (channels, height, width)
168
+ if len(obs_space.shape) == 3:
169
+ cnn = CNN_EXTRACTORS_BY_STYLE[cnn_style](
170
+ obs_space.shape[0],
171
+ activation,
172
+ init_layers_orthogonal=cnn_layers_init_orthogonal,
173
+ impala_channels=impala_channels,
174
+ )
175
+
176
+ def preprocess(obs: torch.Tensor) -> torch.Tensor:
177
+ if len(obs.shape) == 3:
178
+ obs = obs.unsqueeze(0)
179
+ return obs.float() / 255.0
180
+
181
+ with torch.no_grad():
182
+ cnn_out = cnn(preprocess(torch.as_tensor(obs_space.sample())))
183
+ self.preprocess = preprocess
184
+ self.feature_extractor = nn.Sequential(
185
+ cnn,
186
+ layer_init(
187
+ nn.Linear(cnn_out.shape[1], cnn_feature_dim),
188
+ init_layers_orthogonal,
189
+ ),
190
+ activation(),
191
+ )
192
+ self.out_dim = cnn_feature_dim
193
+ elif len(obs_space.shape) == 1:
194
+
195
+ def preprocess(obs: torch.Tensor) -> torch.Tensor:
196
+ if len(obs.shape) == 1:
197
+ obs = obs.unsqueeze(0)
198
+ return obs.float()
199
+
200
+ self.preprocess = preprocess
201
+ self.feature_extractor = nn.Flatten()
202
+ self.out_dim = get_flattened_obs_dim(obs_space)
203
+ else:
204
+ raise ValueError(f"Unsupported observation space: {obs_space}")
205
+ elif isinstance(obs_space, Discrete):
206
+ self.preprocess = lambda x: F.one_hot(x, obs_space.n).float()
207
+ self.feature_extractor = nn.Flatten()
208
+ self.out_dim = obs_space.n
209
+ else:
210
+ raise NotImplementedError
211
+
212
+ def forward(self, obs: torch.Tensor) -> torch.Tensor:
213
+ if self.preprocess:
214
+ obs = self.preprocess(obs)
215
+ return self.feature_extractor(obs)