Aditya Hemant Majali commited on
Commit
91ed57f
·
1 Parent(s): 6bc6939

Push PPO trained LunarLander 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: 280.18 +/- 18.03
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 0x7ff6a6ce94c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff6a6ce9550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff6a6ce95e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff6a6ce9670>", "_build": "<function ActorCriticPolicy._build at 0x7ff6a6ce9700>", "forward": "<function ActorCriticPolicy.forward at 0x7ff6a6ce9790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff6a6ce9820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff6a6ce98b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff6a6ce9940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff6a6ce99d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff6a6ce9a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff6a6ce9af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff6a6d64d40>"}, "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": 1679835535454253763, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "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:f565b08bbbf17fab440a3ae024c662f913cfb9a5008d655e8fb28f6b9d7a1073
3
+ size 147381
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 0x7ff6a6ce94c0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff6a6ce9550>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff6a6ce95e0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff6a6ce9670>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7ff6a6ce9700>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7ff6a6ce9790>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff6a6ce9820>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff6a6ce98b0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7ff6a6ce9940>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff6a6ce99d0>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff6a6ce9a60>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff6a6ce9af0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7ff6a6d64d40>"
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": 1679835535454253763,
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 sha256:8390f590dfe44db83b5806a763426026acc02f8619984aa87d7fefde6d751e4a
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:813890a25f3279f6d147b3222111be3e068f80fce37527c8fe500408e4a4c7ae
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.9.16
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 (208 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 280.1847271760683, "std_reward": 18.02759685681792, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-26T13:33:45.694406"}