snlBro commited on
Commit
a5af6f9
·
1 Parent(s): 91fc729

v1: Upload PPO LunarLander-v2 trained agent

Browse files
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: 250.13 +/- 21.40
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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f6881906d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6881906dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6881906e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6881906ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f6881906f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f688190b040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f688190b0d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f688190b160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f688190b1f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f688190b280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f688190b310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f688190b3a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6881909210>"}, "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": 1677238661147738159, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
ppo-LunarLander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7f130295118e3aec153b6d82411f1a08c8fc199598b3e412551215a1131a092d
3
+ size 147408
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f6881906d30>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6881906dc0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6881906e50>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6881906ee0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f6881906f70>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f688190b040>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f688190b0d0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f688190b160>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f688190b1f0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f688190b280>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f688190b310>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f688190b3a0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f6881909210>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "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",
27
+ "dtype": "float32",
28
+ "_shape": [
29
+ 8
30
+ ],
31
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
32
+ "high": "[inf inf inf inf inf inf inf inf]",
33
+ "bounded_below": "[False False False False False False False False]",
34
+ "bounded_above": "[False False False False False False False False]",
35
+ "_np_random": null
36
+ },
37
+ "action_space": {
38
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
39
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
40
+ "n": 4,
41
+ "_shape": [],
42
+ "dtype": "int64",
43
+ "_np_random": null
44
+ },
45
+ "n_envs": 16,
46
+ "num_timesteps": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1677238661147738159,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": null,
54
+ "lr_schedule": {
55
+ ":type:": "<class 'function'>",
56
+ ":serialized:": "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"
57
+ },
58
+ "_last_obs": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "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"
61
+ },
62
+ "_last_episode_starts": {
63
+ ":type:": "<class 'numpy.ndarray'>",
64
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
65
+ },
66
+ "_last_original_obs": null,
67
+ "_episode_num": 0,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": -0.015808000000000044,
71
+ "ep_info_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "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"
74
+ },
75
+ "ep_success_buffer": {
76
+ ":type:": "<class 'collections.deque'>",
77
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
78
+ },
79
+ "_n_updates": 248,
80
+ "n_steps": 1024,
81
+ "gamma": 0.999,
82
+ "gae_lambda": 0.98,
83
+ "ent_coef": 0.01,
84
+ "vf_coef": 0.5,
85
+ "max_grad_norm": 0.5,
86
+ "batch_size": 64,
87
+ "n_epochs": 4,
88
+ "clip_range": {
89
+ ":type:": "<class 'function'>",
90
+ ":serialized:": "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"
91
+ },
92
+ "clip_range_vf": null,
93
+ "normalize_advantage": true,
94
+ "target_kl": null
95
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a81d3bb1087cd693c2169dad9b1f88548c3bb39529f576cb096e9b8c97b87d8
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:b66602da45dc2d51ecd261694b04dd4cce6e2bbd34c74063c6822ad88d937280
3
+ size 43393
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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.10
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.22.4
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (190 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 250.12833463121692, "std_reward": 21.39994586975368, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-24T12:03:57.055936"}