ksk commited on
Commit
309b8d5
1 Parent(s): 645dc19

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: 242.18 +/- 20.66
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 0x7f28f8f60b80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f28f8f60c10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f28f8f60ca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f28f8f60d30>", "_build": "<function ActorCriticPolicy._build at 0x7f28f8f60dc0>", "forward": "<function ActorCriticPolicy.forward at 0x7f28f8f60e50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f28f8f60ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f28f8f60f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f28f8ee4040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f28f8ee40d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f28f8ee4160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f28f8ee41f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f28f8f612d0>"}, "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": 1676036502986773690, "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 sha256:50a09f87beb1bea6c93eacede6ab503087cfbcd2f8f637625cc913ad15742fdc
3
+ size 147424
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 0x7f28f8f60b80>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f28f8f60c10>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f28f8f60ca0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f28f8f60d30>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f28f8f60dc0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f28f8f60e50>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f28f8f60ee0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f28f8f60f70>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f28f8ee4040>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f28f8ee40d0>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f28f8ee4160>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f28f8ee41f0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f28f8f612d0>"
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": 1676036502986773690,
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:e7b88908da7da574878b9cd1a70c629d4a4da69dcf814919a33418a87bdd286b
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:df5bf3b5c0fac00758e1e877521c0b3fe0af5c34a132b7601c475f46e03b84c4
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 (225 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 242.1810802541258, "std_reward": 20.656138134725047, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-10T14:06:22.798916"}