sgauravm commited on
Commit
ea82b67
·
1 Parent(s): d5ecfb3

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: 175.78 +/- 45.78
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 0x7f0bb072e430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0bb072e4c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0bb072e550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0bb072e5e0>", "_build": "<function ActorCriticPolicy._build at 0x7f0bb072e670>", "forward": "<function ActorCriticPolicy.forward at 0x7f0bb072e700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0bb072e790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0bb072e820>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0bb072e8b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0bb072e940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0bb072e9d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0bb072ea60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0bb07297e0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674774131341225931, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+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 sha256:2a6ab19771c6f8c031130f66439914b4cbd03c0e86bec173a49e045641adbb84
3
+ size 147402
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 0x7f0bb072e430>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0bb072e4c0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0bb072e550>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0bb072e5e0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f0bb072e670>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f0bb072e700>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0bb072e790>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0bb072e820>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f0bb072e8b0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0bb072e940>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0bb072e9d0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0bb072ea60>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f0bb07297e0>"
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": 507904,
47
+ "_total_timesteps": 500000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1674774131341225931,
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": 124,
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:d4d413eff852894cf9e2a8f23b18257631f5f6a27e3195c4b1c24fb4291c5cdc
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1a440b62706651bcf90bebfcb544faeac8b6d13065a1eb2d1b2a817da42dfb3b
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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.10
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
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": 175.78397775599493, "std_reward": 45.77539365288773, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-26T23:14:28.892826"}