mikato commited on
Commit
209f87d
1 Parent(s): 1ea801a

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: 266.38 +/- 16.99
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 0x7efcad4263a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efcad426430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efcad4264c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efcad426550>", "_build": "<function ActorCriticPolicy._build at 0x7efcad4265e0>", "forward": "<function ActorCriticPolicy.forward at 0x7efcad426670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efcad426700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efcad426790>", "_predict": "<function ActorCriticPolicy._predict at 0x7efcad426820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efcad4268b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efcad426940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efcad4269d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7efcad422240>"}, "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": 1676292156072172371, "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "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 sha256:1d6f5ef4fde7a214f0db97f1052254b237a40e4c4f8577f54d5f02fe9388889d
3
+ size 147416
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 0x7efcad4263a0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efcad426430>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efcad4264c0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efcad426550>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7efcad4265e0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7efcad426670>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efcad426700>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efcad426790>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7efcad426820>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efcad4268b0>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efcad426940>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efcad4269d0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7efcad422240>"
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": 1676292156072172371,
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 sha256:3024effb718b51a093833504a68d6ae86f3811ba3b111609d99dbbab924e805f
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0476e56b63515ae44a97b90532840dc02f2e0f71ebf61689e43259225b4dde61
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 (237 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 266.3774075838107, "std_reward": 16.985927582176494, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-13T13:10:34.758621"}