dataLearning commited on
Commit
3b65582
·
1 Parent(s): 6240708

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: 257.26 +/- 15.59
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 0x7f7edb7c3310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7edb7c33a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7edb7c3430>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7edb7c34c0>", "_build": "<function ActorCriticPolicy._build at 0x7f7edb7c3550>", "forward": "<function ActorCriticPolicy.forward at 0x7f7edb7c35e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7edb7c3670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7edb7c3700>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7edb7c3790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7edb7c3820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7edb7c38b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7edb7c3940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7edb7be690>"}, "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": 1675454182794909353, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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 sha256:8da2010090c53c2f84c9cca37d2291f9d2403ecede8dd82e5418e0198bbec602
3
+ size 147420
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 0x7f7edb7c3310>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7edb7c33a0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7edb7c3430>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7edb7c34c0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f7edb7c3550>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f7edb7c35e0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7edb7c3670>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7edb7c3700>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f7edb7c3790>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7edb7c3820>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7edb7c38b0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7edb7c3940>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f7edb7be690>"
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": 1675454182794909353,
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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:cedad2fb3c219fe1af6b5fb02aa98355473c0e60f901e203aff493e43db444f2
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:f722b9e3ea8bbebc47cda4ea0e64f1b370076c2fae7fe0908ef9a621d452d388
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 (202 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 257.26413613331067, "std_reward": 15.592135178794388, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-03T20:21:09.295621"}