menelaos commited on
Commit
41ddad2
·
1 Parent(s): d56fcad

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: 195.76 +/- 74.64
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 0x7f7916911820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f79169118b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7916911940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f79169119d0>", "_build": "<function ActorCriticPolicy._build at 0x7f7916911a60>", "forward": "<function ActorCriticPolicy.forward at 0x7f7916911af0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7916911b80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7916911c10>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7916911ca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7916911d30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7916911dc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7916911e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f791690c9c0>"}, "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": 1674301490695619615, "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-glibc2.29 # 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 sha256:2fee58e92c6b23e1b462d5263283a27e85d30885dde4b2e8fac9a3a5c96bd4ea
3
+ size 147412
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 0x7f7916911820>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f79169118b0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7916911940>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f79169119d0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f7916911a60>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f7916911af0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7916911b80>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7916911c10>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f7916911ca0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7916911d30>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7916911dc0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7916911e50>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f791690c9c0>"
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": 1674301490695619615,
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
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:46c20e9a3bc59c49747a16a3faf4901b0fa738682672aa4c3cb2a9b82cab938d
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:95492414ef1729a47ca1cfa508ff90db403883ba22e5601ca9eaabfa842143b9
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 (249 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 195.7552243694644, "std_reward": 74.63925433940348, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-21T12:11:18.552567"}