bguisard commited on
Commit
a93a4e6
1 Parent(s): 526ec98

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: 254.01 +/- 22.56
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 0x7fe1fc119d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe1fc119dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe1fc119e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe1fc119ee0>", "_build": "<function ActorCriticPolicy._build at 0x7fe1fc119f70>", "forward": "<function ActorCriticPolicy.forward at 0x7fe1fc11f040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe1fc11f0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe1fc11f160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe1fc11f1f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe1fc11f280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe1fc11f310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe1fc11a270>"}, "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": 1670470893184762040, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+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": 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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+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 sha256:9c2db8ec580046383103a09be5bc1a15a47fff68a4a41f108fda504aea5e324e
3
+ size 147210
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7fe1fc119d30>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe1fc119dc0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe1fc119e50>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe1fc119ee0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fe1fc119f70>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fe1fc11f040>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe1fc11f0d0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fe1fc11f160>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe1fc11f1f0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe1fc11f280>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe1fc11f310>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fe1fc11a270>"
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": 1015808,
46
+ "_total_timesteps": 1000000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1670470893184762040,
51
+ "learning_rate": 0.0003,
52
+ "tensorboard_log": null,
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
56
+ },
57
+ "_last_obs": {
58
+ ":type:": "<class 'numpy.ndarray'>",
59
+ ":serialized:": "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"
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.015808000000000044,
70
+ "ep_info_buffer": {
71
+ ":type:": "<class 'collections.deque'>",
72
+ ":serialized:": "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"
73
+ },
74
+ "ep_success_buffer": {
75
+ ":type:": "<class 'collections.deque'>",
76
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
77
+ },
78
+ "_n_updates": 248,
79
+ "n_steps": 1024,
80
+ "gamma": 0.999,
81
+ "gae_lambda": 0.98,
82
+ "ent_coef": 0.01,
83
+ "vf_coef": 0.5,
84
+ "max_grad_norm": 0.5,
85
+ "batch_size": 64,
86
+ "n_epochs": 4,
87
+ "clip_range": {
88
+ ":type:": "<class 'function'>",
89
+ ":serialized:": "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"
90
+ },
91
+ "clip_range_vf": null,
92
+ "normalize_advantage": true,
93
+ "target_kl": null
94
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:730d7ba44ee0f6c4aa859ef9682c8402d28200ff629bd632f609f4dafb6c1bc0
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:673be6751f288a7e8e6070f6586476c7e846912a65340e45cb58321ed7a292ad
3
+ size 43201
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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
2
+ Python: 3.8.15
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 (235 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 254.00512754322912, "std_reward": 22.557731126658766, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T04:06:07.219899"}