Pablinsv commited on
Commit
6c8a32a
1 Parent(s): 59ad194

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: 266.47 +/- 18.25
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 0x7f5919fee9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5919feea60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5919feeaf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5919feeb80>", "_build": "<function ActorCriticPolicy._build at 0x7f5919feec10>", "forward": "<function ActorCriticPolicy.forward at 0x7f5919feeca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5919feed30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5919feedc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5919feee50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5919feeee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5919feef70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5919fe7c30>"}, "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": 1671041705778932943, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAE2ZZT23uTI+XP2MvdvMhr7pgRa9B5WuPQAAAAAAAAAAZk91PQpRLLs2msS7VEuDPCo/XTz4Z2O9AACAPwAAgD8aPAe9q35TP9ZHCbx+Qcq+koSXuwrFvjwAAAAAAAAAAAA4Kj0Bbs+8FTJCPLfHOTwfQT8+QuAOvQAAgD8AAIA/xgKRPoxLaD+27wQ/oRLdvlun0z5iGIw+AAAAAAAAAACa7Us9P7MpPtmfBb4095G+z19/vZfsOLwAAAAAAAAAAAAs+LwfQse7k960OUGigT1AzPU8LhxavAAAgD8AAIA/szgJvT1jG7uB0IA74zmAPBaSF7x22l49AACAPwAAgD8zM4e5xlC5P2dAorqTfzM9IDM/PPYGVzwAAAAAAAAAAGZZvDwU/Im6+6HxtfGx47AG2dS6M08bNQAAgD8AAIA/WojWPVlvCj+qvzK+D3yfvoLIEj0fSYQ7AAAAAAAAAACaZli9ziklPzNcDj6VQsS+w1ZqPNS7uz0AAAAAAAAAADOojjzuqpk9rvgMveT4b74FUxW9e0KQvQAAAAAAAAAAmp/EvOy/mbueOtG7RTmMPJSo4jwYPW+9AACAPwAAgD9mCpm7LKbFPFfKxjy0dFG+JYyivGqWBDwAAAAAAAAAAGbC8jxEMUU+aIphPaTIor7ziNE7+vcRvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": 548, "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:ea3b6bfd247d1d9af99ccd8a2cfcf83e8ba2677330919f19048083f38e8eb48f
3
+ size 147122
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 0x7f5919fee9d0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5919feea60>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5919feeaf0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5919feeb80>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f5919feec10>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f5919feeca0>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5919feed30>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f5919feedc0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5919feee50>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5919feeee0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5919feef70>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f5919fe7c30>"
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": 1671041705778932943,
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": 548,
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:ffd342c084779c58a0d628344b418e22db218adf5be84fdd1ecb0f7a53b01db9
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:bbdd4c1e8c9e17302e73ac8b544397d46e22068fd3627ec1abb111801e8329fc
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 (194 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 266.47217706766145, "std_reward": 18.25017650654361, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-14T18:32:44.079158"}