butchland commited on
Commit
4e91db2
1 Parent(s): 851a1b4

feat: Add first version of RL PPO for LunarLanderV2

Browse files
README.md CHANGED
@@ -1,3 +1,36 @@
1
  ---
2
- license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ - metrics:
12
+ - type: mean_reward
13
+ value: 140.76 +/- 40.85
14
+ name: mean_reward
15
+ task:
16
+ type: reinforcement-learning
17
+ name: reinforcement-learning
18
+ dataset:
19
+ name: LunarLander-v2
20
+ type: LunarLander-v2
21
  ---
22
+
23
+ # **PPO** Agent playing **LunarLander-v2**
24
+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
25
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
26
+
27
+ ## Usage (with Stable-baselines3)
28
+ TODO: Add your code
29
+
30
+
31
+ ```python
32
+ from stable_baselines3 import ...
33
+ from huggingface_sb3 import load_from_hub
34
+
35
+ ...
36
+ ```
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 0x7f87858d0d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f87858d0dd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f87858d0e60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f87858d0ef0>", "_build": "<function ActorCriticPolicy._build at 0x7f87858d0f80>", "forward": "<function ActorCriticPolicy.forward at 0x7f87858d9050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f87858d90e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f87858d9170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f87858d9200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f87858d9290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f87858d9320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f878592c330>"}, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1658839247.243516, "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": 124, "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:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "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 sha256:441696bd4f6bc3c5a0a21332285881166ddb3c4c9198ead28eede7951de19454
3
+ size 147271
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.0
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 0x7f87858d0d40>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f87858d0dd0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f87858d0e60>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f87858d0ef0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f87858d0f80>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f87858d9050>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f87858d90e0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f87858d9170>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f87858d9200>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f87858d9290>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f87858d9320>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f878592c330>"
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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
39
+ "n": 4,
40
+ "_shape": [],
41
+ "dtype": "int64",
42
+ "_np_random": null
43
+ },
44
+ "n_envs": 16,
45
+ "num_timesteps": 507904,
46
+ "_total_timesteps": 500000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1658839247.243516,
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": 124,
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 sha256:4878f851838e1ad0ed978f1a3636192c53418dac0bc874d87cafd3f410ebc4cc
3
+ size 87993
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a24a66c771f3c9c1825efc8df5062661ebdea766878c9bfa6416efd26b06bf68
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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
2
+ Python: 3.7.13
3
+ Stable-Baselines3: 1.6.0
4
+ PyTorch: 1.12.0+cu113
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
replay.mp4 ADDED
Binary file (250 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 140.75676619780455, "std_reward": 40.8466998252487, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-26T14:46:25.388030"}