torayeff commited on
Commit
8414697
·
1 Parent(s): caa5c39

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: 246.19 +/- 17.62
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 0x7f1b7b5ff940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1b7b5ff9d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1b7b5ffa60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1b7b5ffaf0>", "_build": "<function ActorCriticPolicy._build at 0x7f1b7b5ffb80>", "forward": "<function ActorCriticPolicy.forward at 0x7f1b7b5ffc10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1b7b5ffca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1b7b5ffd30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1b7b5ffdc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1b7b5ffe50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1b7b5ffee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1b7b5fff70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1b7b5f6a20>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674415355500803263, "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 sha256:7c217e1d758b59b43dcfcf4cec6682138531fac2bda9e0f32b88e4773936a54d
3
+ size 147426
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 0x7f1b7b5ff940>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1b7b5ff9d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1b7b5ffa60>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1b7b5ffaf0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f1b7b5ffb80>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f1b7b5ffc10>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1b7b5ffca0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1b7b5ffd30>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f1b7b5ffdc0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1b7b5ffe50>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1b7b5ffee0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1b7b5fff70>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f1b7b5f6a20>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
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": 1000000.0,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1674415355500803263,
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 sha256:119c8ff159dd37052b5240fa5c0e3e6ba8706ea69e44dfb8c6b17af694ef5884
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:bd3a30037062a4d84799bffa96a823f3ff731022a731b44e66ed895cfe74a961
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 (209 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 246.19264745164173, "std_reward": 17.61790556738065, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-22T19:51:15.436171"}