cytsai commited on
Commit
dba0801
1 Parent(s): 41f8dc2

Initial commit

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: 249.39 +/- 21.98
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 0x7fcfa82b4c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcfa82b4ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcfa82b4d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcfa82b4dc0>", "_build": "<function ActorCriticPolicy._build at 0x7fcfa82b4e50>", "forward": "<function ActorCriticPolicy.forward at 0x7fcfa82b4ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcfa82b4f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcfa82b9040>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcfa82b90d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcfa82b9160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcfa82b91f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcfa82b9280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcfa82b1930>"}, "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": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673920091905676187, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
first_ppo_ml_model.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:99c1aa5fd785998fe9733331be781e2a7c6bf95ce7eefb749dc4322062ed2c6e
3
+ size 147422
first_ppo_ml_model/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
first_ppo_ml_model/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 0x7fcfa82b4c10>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcfa82b4ca0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcfa82b4d30>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcfa82b4dc0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fcfa82b4e50>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fcfa82b4ee0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcfa82b4f70>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcfa82b9040>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fcfa82b90d0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcfa82b9160>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcfa82b91f0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcfa82b9280>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7fcfa82b1930>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
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": 1000000.0,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1673920091905676187,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": null,
54
+ "lr_schedule": {
55
+ ":type:": "<class 'function'>",
56
+ ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
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
+ }
first_ppo_ml_model/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:998a359fe657658fac0018879550d094d528b27ee46d92370d6fa8c548af0b79
3
+ size 87929
first_ppo_ml_model/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a04e68f5ada4ae986dddb7c7edae5a5c6b61f8b12a2bc189521187d51b720765
3
+ size 43393
first_ppo_ml_model/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
first_ppo_ml_model/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.16
3
+ - Stable-Baselines3: 1.7.0
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 (223 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 249.3920701460964, "std_reward": 21.977017059413033, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-17T02:12:52.415056"}