ScareCrow432 commited on
Commit
b695f26
1 Parent(s): 301ee72

added Trained model

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: 259.49 +/- 21.87
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 0x7f6892c53040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6892c530d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6892c53160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6892c531f0>", "_build": "<function ActorCriticPolicy._build at 0x7f6892c53280>", "forward": "<function ActorCriticPolicy.forward at 0x7f6892c53310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6892c533a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6892c53430>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6892c534c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6892c53550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6892c535e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6892c53670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6892c4d6c0>"}, "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": 1675162068037467600, "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:a6620d5b2800a04734b70169172bef63258ab4983279ed070e62aa28297383ca
3
+ size 147348
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 0x7f6892c53040>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6892c530d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6892c53160>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6892c531f0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f6892c53280>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f6892c53310>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6892c533a0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6892c53430>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f6892c534c0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6892c53550>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6892c535e0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6892c53670>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f6892c4d6c0>"
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": 1675162068037467600,
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 sha256:32525c210acd233e66ee49ac6509e8e4acc209c72e9a074342853eadea136a9e
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:83a0448de0979a1238a04686bb8d71d5987f1de15bb566ffcfb0d7c216f950d9
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 (200 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 259.4930690805978, "std_reward": 21.867162706770845, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-31T11:09:08.861531"}