eikoenchine commited on
Commit
834a081
1 Parent(s): 1c71a3c

trained 1e7 timesteps

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: 296.11 +/- 13.19
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 0x7d5d6c7c4940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d5d6c7c49d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d5d6c7c4a60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d5d6c7c4af0>", "_build": "<function ActorCriticPolicy._build at 0x7d5d6c7c4b80>", "forward": "<function ActorCriticPolicy.forward at 0x7d5d6c7c4c10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d5d6c7c4ca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d5d6c7c4d30>", "_predict": "<function ActorCriticPolicy._predict at 0x7d5d6c7c4dc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d5d6c7c4e50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d5d6c7c4ee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d5d6c7c4f70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d5d6c7b1bc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 10010624, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690324741296131451, "learning_rate": 0.0003, "tensorboard_log": null, "_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": -0.0010623999999999079, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2471, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
replay.mp4 ADDED
Binary file (159 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 296.1148889, "std_reward": 13.18529130799549, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-26T00:32:33.268395"}
test.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6db06d4ca0a2734114536404c27037f247bb437e586e17869cc7215f049adfd0
3
+ size 146629
test/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 2.0.0a5
test/data ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7d5d6c7c4940>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d5d6c7c49d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d5d6c7c4a60>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d5d6c7c4af0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7d5d6c7c4b80>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7d5d6c7c4c10>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d5d6c7c4ca0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d5d6c7c4d30>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7d5d6c7c4dc0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d5d6c7c4e50>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d5d6c7c4ee0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d5d6c7c4f70>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7d5d6c7b1bc0>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "num_timesteps": 10010624,
25
+ "_total_timesteps": 10000000,
26
+ "_num_timesteps_at_start": 0,
27
+ "seed": null,
28
+ "action_noise": null,
29
+ "start_time": 1690324741296131451,
30
+ "learning_rate": 0.0003,
31
+ "tensorboard_log": null,
32
+ "_last_obs": {
33
+ ":type:": "<class 'numpy.ndarray'>",
34
+ ":serialized:": "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"
35
+ },
36
+ "_last_episode_starts": {
37
+ ":type:": "<class 'numpy.ndarray'>",
38
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
39
+ },
40
+ "_last_original_obs": null,
41
+ "_episode_num": 0,
42
+ "use_sde": false,
43
+ "sde_sample_freq": -1,
44
+ "_current_progress_remaining": -0.0010623999999999079,
45
+ "_stats_window_size": 100,
46
+ "ep_info_buffer": {
47
+ ":type:": "<class 'collections.deque'>",
48
+ ":serialized:": "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"
49
+ },
50
+ "ep_success_buffer": {
51
+ ":type:": "<class 'collections.deque'>",
52
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
53
+ },
54
+ "_n_updates": 2471,
55
+ "observation_space": {
56
+ ":type:": "<class 'gymnasium.spaces.box.Box'>",
57
+ ":serialized:": "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",
58
+ "dtype": "float32",
59
+ "bounded_below": "[ True True True True True True True True]",
60
+ "bounded_above": "[ True True True True True True True True]",
61
+ "_shape": [
62
+ 8
63
+ ],
64
+ "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
65
+ "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
66
+ "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
67
+ "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
68
+ "_np_random": null
69
+ },
70
+ "action_space": {
71
+ ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
72
+ ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
73
+ "n": "4",
74
+ "start": "0",
75
+ "_shape": [],
76
+ "dtype": "int64",
77
+ "_np_random": null
78
+ },
79
+ "n_envs": 16,
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
+ "lr_schedule": {
96
+ ":type:": "<class 'function'>",
97
+ ":serialized:": "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"
98
+ }
99
+ }
test/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:13556653ca6fce4986a1dbbf05d9e32fb12757b806b5505aec80c4a453fe7221
3
+ size 87929
test/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e9a800a2b1a0b074db7e54dfa9827aec26202d5f467024edf4ef051081cf83f6
3
+ size 43329
test/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
test/system_info.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
2
+ - Python: 3.10.6
3
+ - Stable-Baselines3: 2.0.0a5
4
+ - PyTorch: 2.0.1+cu118
5
+ - GPU Enabled: True
6
+ - Numpy: 1.22.4
7
+ - Cloudpickle: 2.2.1
8
+ - Gymnasium: 0.28.1
9
+ - OpenAI Gym: 0.25.2