jjpp3301 commited on
Commit
ca4b3e9
1 Parent(s): e62747c

first trained agent with proximal policy optimization

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: 286.12 +/- 17.95
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 0x7f705cdad820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f705cdad8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f705cdad940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f705cdad9d0>", "_build": "<function ActorCriticPolicy._build at 0x7f705cdada60>", "forward": "<function ActorCriticPolicy.forward at 0x7f705cdadaf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f705cdadb80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f705cdadc10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f705cdadca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f705cdadd30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f705cdaddc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f705cda6f60>"}, "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": 2031616, "_total_timesteps": 2000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670807029955396540, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 940, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}
lunar_lander_ppo.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a71ef11ca34e2bb13a2594716839ff42c501b520a6d5817e842a49e68b30a28f
3
+ size 146284
lunar_lander_ppo/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
lunar_lander_ppo/data ADDED
@@ -0,0 +1,91 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7f705cdad820>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f705cdad8b0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f705cdad940>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f705cdad9d0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f705cdada60>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f705cdadaf0>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f705cdadb80>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f705cdadc10>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f705cdadca0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f705cdadd30>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f705cdaddc0>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f705cda6f60>"
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": 2031616,
46
+ "_total_timesteps": 2000000.0,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1670807029955396540,
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": null,
58
+ "_last_episode_starts": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
61
+ },
62
+ "_last_original_obs": null,
63
+ "_episode_num": 0,
64
+ "use_sde": false,
65
+ "sde_sample_freq": -1,
66
+ "_current_progress_remaining": -0.015808000000000044,
67
+ "ep_info_buffer": {
68
+ ":type:": "<class 'collections.deque'>",
69
+ ":serialized:": "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"
70
+ },
71
+ "ep_success_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
74
+ },
75
+ "_n_updates": 940,
76
+ "n_steps": 2048,
77
+ "gamma": 0.99,
78
+ "gae_lambda": 0.95,
79
+ "ent_coef": 0.0,
80
+ "vf_coef": 0.5,
81
+ "max_grad_norm": 0.5,
82
+ "batch_size": 64,
83
+ "n_epochs": 10,
84
+ "clip_range": {
85
+ ":type:": "<class 'function'>",
86
+ ":serialized:": "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"
87
+ },
88
+ "clip_range_vf": null,
89
+ "normalize_advantage": true,
90
+ "target_kl": null
91
+ }
lunar_lander_ppo/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d4d546aaa07b153ba2b6f65cbc09e2bc3481cb1089519e64cf0551fd7ad9bd64
3
+ size 88057
lunar_lander_ppo/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ab3bf9e2b48370da896e56bf591b4c66cbe107703fc5308a6a601f931e03d9d7
3
+ size 43201
lunar_lander_ppo/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
lunar_lander_ppo/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 (221 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 286.12453207862944, "std_reward": 17.950547966482564, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-12T02:05:07.148364"}