PowerLine49
commited on
Commit
•
0e399fe
1
Parent(s):
7565c43
Upload PPO LunarLander-v2 Trained Agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo_LunarLander-v2.zip +3 -0
- ppo_LunarLander-v2/_stable_baselines3_version +1 -0
- ppo_LunarLander-v2/data +94 -0
- ppo_LunarLander-v2/policy.optimizer.pth +3 -0
- ppo_LunarLander-v2/policy.pth +3 -0
- ppo_LunarLander-v2/pytorch_variables.pth +3 -0
- ppo_LunarLander-v2/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- LunarLander-v2
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: PPO
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: LunarLander-v2
|
16 |
+
type: LunarLander-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: 254.50 +/- 14.49
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f61554c0ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f61554c0d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f61554c0dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f61554c0e50>", "_build": "<function ActorCriticPolicy._build at 0x7f61554c0ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f61554c0f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f61554c5040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f61554c50d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f61554c5160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f61554c51f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f61554c5280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f61554bd420>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671583137031678445, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo_LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7573b105387f06b949299a9834dfaa2bfd0e863064311ed105236eee39efeb08
|
3 |
+
size 147206
|
ppo_LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.6.2
|
ppo_LunarLander-v2/data
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
+
"__module__": "stable_baselines3.common.policies",
|
6 |
+
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f61554c0ca0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f61554c0d30>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f61554c0dc0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f61554c0e50>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f61554c0ee0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f61554c0f70>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f61554c5040>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f61554c50d0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f61554c5160>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f61554c51f0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f61554c5280>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f61554bd420>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "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",
|
26 |
+
"dtype": "float32",
|
27 |
+
"_shape": [
|
28 |
+
8
|
29 |
+
],
|
30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
32 |
+
"bounded_below": "[False False False False False False False False]",
|
33 |
+
"bounded_above": "[False False False False False False False False]",
|
34 |
+
"_np_random": null
|
35 |
+
},
|
36 |
+
"action_space": {
|
37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
38 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
39 |
+
"n": 4,
|
40 |
+
"_shape": [],
|
41 |
+
"dtype": "int64",
|
42 |
+
"_np_random": null
|
43 |
+
},
|
44 |
+
"n_envs": 16,
|
45 |
+
"num_timesteps": 1015808,
|
46 |
+
"_total_timesteps": 1000000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1671583137031678445,
|
51 |
+
"learning_rate": 0.0003,
|
52 |
+
"tensorboard_log": null,
|
53 |
+
"lr_schedule": {
|
54 |
+
":type:": "<class 'function'>",
|
55 |
+
":serialized:": "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"
|
56 |
+
},
|
57 |
+
"_last_obs": {
|
58 |
+
":type:": "<class 'numpy.ndarray'>",
|
59 |
+
":serialized:": "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"
|
60 |
+
},
|
61 |
+
"_last_episode_starts": {
|
62 |
+
":type:": "<class 'numpy.ndarray'>",
|
63 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
64 |
+
},
|
65 |
+
"_last_original_obs": null,
|
66 |
+
"_episode_num": 0,
|
67 |
+
"use_sde": false,
|
68 |
+
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -0.015808000000000044,
|
70 |
+
"ep_info_buffer": {
|
71 |
+
":type:": "<class 'collections.deque'>",
|
72 |
+
":serialized:": "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"
|
73 |
+
},
|
74 |
+
"ep_success_buffer": {
|
75 |
+
":type:": "<class 'collections.deque'>",
|
76 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
+
},
|
78 |
+
"_n_updates": 248,
|
79 |
+
"n_steps": 1024,
|
80 |
+
"gamma": 0.999,
|
81 |
+
"gae_lambda": 0.98,
|
82 |
+
"ent_coef": 0.01,
|
83 |
+
"vf_coef": 0.5,
|
84 |
+
"max_grad_norm": 0.5,
|
85 |
+
"batch_size": 64,
|
86 |
+
"n_epochs": 4,
|
87 |
+
"clip_range": {
|
88 |
+
":type:": "<class 'function'>",
|
89 |
+
":serialized:": "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"
|
90 |
+
},
|
91 |
+
"clip_range_vf": null,
|
92 |
+
"normalize_advantage": true,
|
93 |
+
"target_kl": null
|
94 |
+
}
|
ppo_LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1742a8036c12c1b898d534b6d3c72c4ed59e052f157229d6214f8ec3fd1855f0
|
3 |
+
size 87929
|
ppo_LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:46e4b89cea242d89846c791240316e74848453c6802d5fe4b1059d8ded563d34
|
3 |
+
size 43201
|
ppo_LunarLander-v2/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
ppo_LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
2 |
+
Python: 3.8.16
|
3 |
+
Stable-Baselines3: 1.6.2
|
4 |
+
PyTorch: 1.13.0+cu116
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (236 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 254.49974078046603, "std_reward": 14.485488734883873, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-21T01:30:34.025587"}
|