pushing model

README.md

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+---
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+library_name: cleanrl
+model-index:
+- name: DQPN_freq
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+    metrics:
+    - type: mean_reward
+      value: 343.71 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
+
+This is a trained model of a DQPN_freq agent playing CartPole-v1.
+The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
+found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_freq_5000_0.99.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[DQPN_freq_5000_0.99]"
+python -m cleanrl_utils.enjoy --exp-name DQPN_freq_5000_0.99 --env-id CartPole-v1
+```
+
+Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
+
+
+## Command to reproduce the training
+
+```bash
+curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_freq_5000_0.99-seed3/raw/main/dqpn_freq.py
+curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_freq_5000_0.99-seed3/raw/main/pyproject.toml
+curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_freq_5000_0.99-seed3/raw/main/poetry.lock
+poetry install --all-extras
+python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQPN_freq_5000_0.99 --gamma 0.99 --policy-network-frequency 5000 --seed 3
+```
+
+# Hyperparameters
+```python
+{'alg_type': 'dqpn_freq.py',
+ 'batch_size': 256,
+ 'buffer_size': 300000,
+ 'capture_video': True,
+ 'cuda': True,
+ 'end_e': 0.1,
+ 'env_id': 'CartPole-v1',
+ 'exp_name': 'DQPN_freq_5000_0.99',
+ 'exploration_fraction': 0.2,
+ 'gamma': 0.99,
+ 'hf_entity': 'pfunk',
+ 'learning_rate': 0.0001,
+ 'learning_starts': 1000,
+ 'policy_network_frequency': 5000,
+ 'policy_tau': 1.0,
+ 'save_model': True,
+ 'seed': 3,
+ 'start_e': 1.0,
+ 'target_network_frequency': 20,
+ 'target_tau': 1.0,
+ 'torch_deterministic': True,
+ 'total_timesteps': 500000,
+ 'track': True,
+ 'train_frequency': 1,
+ 'upload_model': True,
+ 'wandb_entity': 'pfunk',
+ 'wandb_project_name': 'dqpn'}
+```
+    

dqpn_freq.py

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+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqnpy
+import argparse
+import os
+import random
+import time
+from distutils.util import strtobool
+
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from stable_baselines3.common.buffers import ReplayBuffer
+from torch.utils.tensorboard import SummaryWriter
+
+
+def parse_args():
+    # fmt: off
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help="the name of this experiment")
+    parser.add_argument("--seed", type=int, default=1,
+        help="seed of the experiment")
+    parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, `torch.backends.cudnn.deterministic=False`")
+    parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, cuda will be enabled by default")
+    parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="if toggled, this experiment will be tracked with Weights and Biases")
+    parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument("--wandb-entity", type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to save model into the `runs/{run_name}` folder")
+    parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to upload the saved model to huggingface")
+    parser.add_argument("--hf-entity", type=str, default="",
+        help="the user or org name of the model repository from the Hugging Face Hub")
+
+    # Algorithm specific arguments
+    parser.add_argument("--env-id", type=str, default="CartPole-v1",
+        help="the id of the environment")
+    parser.add_argument("--total-timesteps", type=int, default=500000,
+        help="total timesteps of the experiments")
+    parser.add_argument("--learning-rate", type=float, default=0.0001,
+        help="the learning rate of the optimizer")
+    parser.add_argument("--buffer-size", type=int, default=300000,
+        help="the replay memory buffer size")
+    parser.add_argument("--gamma", type=float, default=1.0,
+        help="the discount factor gamma")
+    parser.add_argument("--target-tau", type=float, default=1.0,
+        help="the target network update rate")
+    parser.add_argument("--policy-tau", type=float, default=1.0,
+        help="the target network update rate")
+    parser.add_argument("--target-network-frequency", type=int, default=20,
+        help="the timesteps it takes to update the target network")
+    parser.add_argument("--policy-network-frequency", type=int, default=1000,
+        help="the timesteps it takes to update the policy network")
+    parser.add_argument("--batch-size", type=int, default=256,
+        help="the batch size of sample from the reply memory")
+    parser.add_argument("--start-e", type=float, default=1.0,
+        help="the starting epsilon for exploration")
+    parser.add_argument("--end-e", type=float, default=0.1,
+        help="the ending epsilon for exploration")
+    parser.add_argument("--exploration-fraction", type=float, default=0.2,
+        help="the fraction of `total-timesteps` it takes from start-e to go end-e")
+    parser.add_argument("--learning-starts", type=int, default=1000,
+        help="timestep to start learning")
+    parser.add_argument("--train-frequency", type=int, default=1,
+        help="the frequency of training")
+    args = parser.parse_args()
+    # fmt: on
+    return args
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+
+    return thunk
+
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Linear(np.array(env.single_observation_space.shape).prod(), 512),
+            nn.ReLU(),
+            nn.Linear(512, 128),
+            nn.ReLU(),
+            nn.Linear(128, env.single_action_space.n),
+        )
+
+    def forward(self, x):
+        return self.network(x)
+
+
+def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
+    slope = (end_e - start_e) / duration
+    return max(slope * t + start_e, end_e)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        args.alg_type = os.path.basename(__file__)
+        wandb_sess = wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            config=vars(args),
+            save_code=True,
+            # group='string',
+            name=run_name,
+            sync_tensorboard=False,
+            monitor_gym=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    def log_value(name: str, x: float, y: int):
+        # writer.add_scalar(name, x, y)
+        wandb.log({name: x, "global_step": y})
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    # env setup
+    envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+    envs.seed(args.seed)
+
+    q_network = QNetwork(envs).to(device)
+    optimizer = optim.RMSprop(q_network.parameters(), lr=args.learning_rate)
+    target_network = QNetwork(envs).to(device)
+    policy_network = QNetwork(envs).to(device)
+    target_network.load_state_dict(q_network.state_dict())
+    policy_network.load_state_dict(q_network.state_dict())
+
+    rb = ReplayBuffer(
+        args.buffer_size,
+        envs.single_observation_space,
+        envs.single_action_space,
+        device,
+        optimize_memory_usage=True,
+        handle_timeout_termination=True,
+    )
+    start_time = time.time()
+    policy_update_counter = 0
+    episode_returns = []
+
+    # TRY NOT TO MODIFY: start the game
+    obs = envs.reset()
+    for global_step in range(args.total_timesteps):
+        # ALGO LOGIC: put action logic here
+        epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
+
+        if random.random() < epsilon:
+            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
+        else:
+            q_values = policy_network(torch.Tensor(obs).to(device))
+            actions = torch.argmax(q_values, dim=1).cpu().numpy()
+
+        # TRY NOT TO MODIFY: execute the game and log data.
+        next_obs, rewards, dones, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        for info in infos:
+            if "episode" in info.keys():
+                episode_returns.append(info['episode']['r'])
+                episode_returns = episode_returns[-100:]
+                print(f"step={global_step}, return={info['episode']['r']}, sps={int(global_step / (time.time() - start_time))}")
+                log_value("perf/episodic_return", info["episode"]["r"], global_step)
+                log_value("perf/episodic_return_mean_100", np.mean(episode_returns), global_step)
+                log_value("perf/episodic_return_std_100", np.std(episode_returns), global_step)
+                log_value("debug/episodic_length", info["episode"]["l"], global_step)
+                log_value("ex2/epsilon", epsilon, global_step)
+                break
+
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
+        real_next_obs = next_obs.copy()
+        for idx, d in enumerate(dones):
+            if d:
+                real_next_obs[idx] = infos[idx]["terminal_observation"]
+        rb.add(obs, real_next_obs, actions, rewards, dones, infos)
+
+        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
+        obs = next_obs
+
+        # ALGO LOGIC: training.
+        if global_step > args.learning_starts:
+            # NOTE: Current code does not work with train_frequency != 1
+            if global_step % args.train_frequency == 0:
+                data = rb.sample(args.batch_size)
+                with torch.no_grad():
+                    target_max, _ = target_network(data.next_observations).max(dim=1)
+                    td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())
+                old_val = q_network(data.observations).gather(1, data.actions).squeeze()
+                loss = F.mse_loss(td_target, old_val)
+
+                if global_step % 100 == 0:
+
+                    prev = old_val.detach().cpu().numpy()
+                    new = td_target.detach().cpu().numpy()
+                    diff, a_diff = new-prev, np.abs(new-prev)
+
+                    mean, a_mean = np.mean(diff), np.mean(a_diff)
+                    median, a_median = np.median(diff), np.median(a_diff)
+                    maximum, a_maximum = np.max(diff), np.max(a_diff)
+                    minimum, a_minimum = np.min(diff), np.min(a_diff)
+                    std, a_std = np.std(diff), np.std(a_diff)
+                    below, a_below = mean - std, a_mean - a_std
+                    above, a_above = mean + std, a_mean + a_std
+                    pu_scalar, a_pu_scalar = 2 * mean / maximum, 2 * a_mean / a_maximum
+                    policy_frequency_scalar_ratio = args.policy_network_frequency * pu_scalar
+                    a_policy_frequency_scalar_ratio = args.policy_network_frequency * a_pu_scalar
+
+                    log_value("losses/td_loss", loss, global_step)
+                    log_value("losses/q_values", old_val.mean().item(), global_step)
+                    log_value("td/mean", mean, global_step)
+                    log_value("td/a_mean", a_mean, global_step)
+                    log_value("td/median", median, global_step)
+                    log_value("td/a_median", a_median, global_step)
+                    log_value("td/max", maximum, global_step)
+                    log_value("td/a_max", a_maximum, global_step)
+                    log_value("td/min", minimum, global_step)
+                    log_value("td/a_min", a_minimum, global_step)
+                    log_value("td/std", std, global_step)
+                    log_value("td/a_std", a_std, global_step)
+                    log_value("td/below", below, global_step)
+                    log_value("td/a_below", a_below, global_step)
+                    log_value("td/above", above, global_step)
+                    log_value("td/a_above", a_above, global_step)
+                    log_value("pu/pu_scalar", pu_scalar, global_step)
+                    log_value("pu/a_pu_scalar", a_pu_scalar, global_step)
+                    log_value("pu/policy_frequency_scalar_ratio", policy_frequency_scalar_ratio, global_step)
+                    log_value("pu/a_policy_frequency_scalar_ratio", a_policy_frequency_scalar_ratio, global_step)
+                    log_value("debug/steps_per_second", int(global_step / (time.time() - start_time)), global_step)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+            # update target network
+            if global_step % args.target_network_frequency == 0:
+                for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
+                    target_network_param.data.copy_(
+                        args.target_tau * q_network_param.data + (1.0 - args.target_tau) * target_network_param.data
+                    )
+
+            # update policy network
+            if global_step % args.policy_network_frequency == 0:
+                policy_update_counter += 1
+                for policy_network_param, q_network_param in zip(policy_network.parameters(), q_network.parameters()):
+                    policy_network_param.data.copy_(
+                        args.policy_tau * q_network_param.data + (1.0 - args.policy_tau) * policy_network_param.data
+                    )
+                for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
+                    target_network_param.data.copy_(
+                        args.target_tau * q_network_param.data + (1.0 - args.target_tau) * target_network_param.data
+                    )
+            if global_step % 100 == 0:
+                log_value("pu/n_policy_update", policy_update_counter, global_step)
+
+    if args.save_model:
+        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
+        torch.save(policy_network.state_dict(), model_path)
+        print(f"model saved to {model_path}")
+        from cleanrl_utils.evals.dqn_eval import evaluate
+
+        episodic_returns = evaluate(
+            model_path,
+            make_env,
+            args.env_id,
+            eval_episodes=10,
+            run_name=f"{run_name}-eval",
+            Model=QNetwork,
+            device=device,
+            epsilon=0.05,
+        )
+        for idx, episodic_return in enumerate(episodic_returns):
+            log_value("eval/episodic_return", episodic_return, idx)
+
+
+        if args.upload_model:
+            from cleanrl_utils.huggingface import push_to_hub
+
+            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
+            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
+            push_to_hub(args, np.mean(episode_returns), repo_id, "DQPN_freq", f"runs/{run_name}", f"videos/{run_name}-eval")
+
+    wandb_sess.finish()
+    envs.close()
+    writer.close()

events.out.tfevents.1679115745.rhea.136041.0

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+size 696

pyproject.toml

@@ -0,0 +1,178 @@
+[tool.poetry]
+name = "cleanrl"
+version = "1.1.0"
+description = "High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features"
+authors = ["Costa Huang <costa.huang@outlook.com>"]
+packages = [
+    { include = "cleanrl" },
+    { include = "cleanrl_utils" },
+]
+keywords = ["reinforcement", "machine", "learning", "research"]
+license="MIT"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.7.1,<3.10"
+tensorboard = "^2.10.0"
+wandb = "^0.13.6"
+gym = "0.23.1"
+torch = ">=1.12.1"
+stable-baselines3 = "1.2.0"
+gymnasium = "^0.26.3"
+moviepy = "^1.0.3"
+pygame = "2.1.0"
+huggingface-hub = "^0.11.1"
+
+ale-py = {version = "0.7.4", optional = true}
+AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2"}
+opencv-python = {version = "^4.6.0.66", optional = true}
+pybullet = {version = "3.1.8", optional = true}
+procgen = {version = "^0.10.7", optional = true}
+pytest = {version = "^7.1.3", optional = true}
+mujoco = {version = "^2.2", optional = true}
+imageio = {version = "^2.14.1", optional = true}
+free-mujoco-py = {version = "^2.1.6", optional = true}
+mkdocs-material = {version = "^8.4.3", optional = true}
+markdown-include = {version = "^0.7.0", optional = true}
+jax = {version = "^0.3.17", optional = true}
+jaxlib = {version = "^0.3.15", optional = true}
+flax = {version = "^0.6.0", optional = true}
+optuna = {version = "^3.0.1", optional = true}
+optuna-dashboard = {version = "^0.7.2", optional = true}
+rich = {version = "<12.0", optional = true}
+envpool = {version = "^0.6.4", optional = true}
+PettingZoo = {version = "1.18.1", optional = true}
+SuperSuit = {version = "3.4.0", optional = true}
+multi-agent-ale-py = {version = "0.1.11", optional = true}
+boto3 = {version = "^1.24.70", optional = true}
+awscli = {version = "^1.25.71", optional = true}
+shimmy = {version = "^0.1.0", optional = true}
+dm-control = {version = "^1.0.8", optional = true}
+
+[tool.poetry.group.dev.dependencies]
+pre-commit = "^2.20.0"
+
+[tool.poetry.group.atari]
+optional = true
+[tool.poetry.group.atari.dependencies]
+ale-py = "0.7.4"
+AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2"}
+opencv-python = "^4.6.0.66"
+
+[tool.poetry.group.pybullet]
+optional = true
+[tool.poetry.group.pybullet.dependencies]
+pybullet = "3.1.8"
+
+[tool.poetry.group.procgen]
+optional = true
+[tool.poetry.group.procgen.dependencies]
+procgen = "^0.10.7"
+
+[tool.poetry.group.pytest]
+optional = true
+[tool.poetry.group.pytest.dependencies]
+pytest = "^7.1.3"
+
+[tool.poetry.group.mujoco]
+optional = true
+[tool.poetry.group.mujoco.dependencies]
+mujoco = "^2.2"
+imageio = "^2.14.1"
+
+[tool.poetry.group.mujoco_py]
+optional = true
+[tool.poetry.group.mujoco_py.dependencies]
+free-mujoco-py = "^2.1.6"
+
+[tool.poetry.group.docs]
+optional = true
+[tool.poetry.group.docs.dependencies]
+mkdocs-material = "^8.4.3"
+markdown-include = "^0.7.0"
+
+[tool.poetry.group.jax]
+optional = true
+[tool.poetry.group.jax.dependencies]
+jax = "^0.3.17"
+jaxlib = "^0.3.15"
+flax = "^0.6.0"
+
+[tool.poetry.group.optuna]
+optional = true
+[tool.poetry.group.optuna.dependencies]
+optuna = "^3.0.1"
+optuna-dashboard = "^0.7.2"
+rich = "<12.0"
+
+[tool.poetry.group.envpool]
+optional = true
+[tool.poetry.group.envpool.dependencies]
+envpool = "^0.6.4"
+
+[tool.poetry.group.pettingzoo]
+optional = true
+[tool.poetry.group.pettingzoo.dependencies]
+PettingZoo = "1.18.1"
+SuperSuit = "3.4.0"
+multi-agent-ale-py = "0.1.11"
+
+[tool.poetry.group.cloud]
+optional = true
+[tool.poetry.group.cloud.dependencies]
+boto3 = "^1.24.70"
+awscli = "^1.25.71"
+
+[tool.poetry.group.isaacgym]
+optional = true
+[tool.poetry.group.isaacgym.dependencies]
+isaacgymenvs = {git = "https://github.com/vwxyzjn/IsaacGymEnvs.git", rev = "poetry"}
+isaacgym = {path = "cleanrl/ppo_continuous_action_isaacgym/isaacgym", develop = true}
+
+[tool.poetry.group.dm_control]
+optional = true
+[tool.poetry.group.dm_control.dependencies]
+shimmy = "^0.1.0"
+dm-control = "^1.0.8"
+mujoco = "^2.2"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.extras]
+atari = ["ale-py", "AutoROM", "opencv-python"]
+pybullet = ["pybullet"]
+procgen = ["procgen"]
+plot = ["pandas", "seaborn"]
+pytest = ["pytest"]
+mujoco = ["mujoco", "imageio"]
+mujoco_py = ["free-mujoco-py"]
+jax = ["jax", "jaxlib", "flax"]
+docs = ["mkdocs-material", "markdown-include"]
+envpool = ["envpool"]
+optuna = ["optuna", "optuna-dashboard", "rich"]
+pettingzoo = ["PettingZoo", "SuperSuit", "multi-agent-ale-py"]
+cloud = ["boto3", "awscli"]
+dm_control = ["shimmy", "dm-control", "mujoco"]
+
+# dependencies for algorithm variant (useful when you want to run a specific algorithm)
+dqn = []
+dqn_atari = ["ale-py", "AutoROM", "opencv-python"]
+dqn_jax = ["jax", "jaxlib", "flax"]
+dqn_atari_jax = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax" # jax
+]
+c51 = []
+c51_atari = ["ale-py", "AutoROM", "opencv-python"]
+c51_jax = ["jax", "jaxlib", "flax"]
+c51_atari_jax = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax" # jax
+]
+ppo_atari_envpool_xla_jax_scan = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax", # jax
+    "envpool", # envpool
+]