amsh commited on
Commit
83f67a3
·
1 Parent(s): af9d0ae

Uploading PPO environment and LunarLanderv2 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: LunarLander-v2-xla1118
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: 265.43 +/- 21.47
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **LunarLander-v2-xla1118** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **LunarLander-v2-xla1118** 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 0x7f447886b040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f447886b0d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f447886b160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f447886b1f0>", "_build": "<function ActorCriticPolicy._build at 0x7f447886b280>", "forward": "<function ActorCriticPolicy.forward at 0x7f447886b310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f447886b3a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f447886b430>", "_predict": "<function ActorCriticPolicy._predict at 0x7f447886b4c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f447886b550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f447886b5e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f447886b670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f44799a2fc0>"}, "verbose": 2, "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": 1678508354818577391, "learning_rate": 0.0003, "tensorboard_log": "/content/drive/MyDrive/StableBaseline3_PPO/", "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": 740, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.25, "max_grad_norm": 0.25, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
ppo-LunarLander-v2-xla1118.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a73dc86d5cc21ac5a61a1715677ff4c8cd7902e36f68b0592582fc9709ea4037
3
+ size 147480
ppo-LunarLander-v2-xla1118/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-LunarLander-v2-xla1118/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 0x7f447886b040>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f447886b0d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f447886b160>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f447886b1f0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f447886b280>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f447886b310>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f447886b3a0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f447886b430>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f447886b4c0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f447886b550>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f447886b5e0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f447886b670>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7f44799a2fc0>"
21
+ },
22
+ "verbose": 2,
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": 1678508354818577391,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": "/content/drive/MyDrive/StableBaseline3_PPO/",
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": 740,
80
+ "n_steps": 1024,
81
+ "gamma": 0.999,
82
+ "gae_lambda": 0.98,
83
+ "ent_coef": 0.01,
84
+ "vf_coef": 0.25,
85
+ "max_grad_norm": 0.25,
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-xla1118/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:83215b8369c513034eb55dd72a559353078c92edc0d4185b5cfb9343885460a1
3
+ size 88057
ppo-LunarLander-v2-xla1118/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3f49931ab051086c581cc465743d20cd82997bd14df109f6bf8ec39b748741c8
3
+ size 43393
ppo-LunarLander-v2-xla1118/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-xla1118/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.9.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.22.4
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (200 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 265.42748844804726, "std_reward": 21.46549548329668, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-11T04:49:19.620554"}