gautierviaud commited on
Commit
bb4155c
·
1 Parent(s): 1902f08

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: 250.41 +/- 14.20
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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f65ec8bff70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f65ec8c3040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f65ec8c30d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65ec8c3160>", "_build": "<function ActorCriticPolicy._build at 0x7f65ec8c31f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f65ec8c3280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f65ec8c3310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f65ec8c33a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f65ec8c3430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f65ec8c34c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f65ec8c3550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f65ec8be8a0>"}, "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": 1672148170717610015, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
fire-ostrich.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b682dfd7d3d3b24a2e7413e639c9052585ab12abb62d41b741b6107f53a9fdee
3
+ size 147210
fire-ostrich/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
fire-ostrich/data ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f65ec8bff70>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f65ec8c3040>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f65ec8c30d0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65ec8c3160>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f65ec8c31f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f65ec8c3280>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f65ec8c3310>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f65ec8c33a0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f65ec8c3430>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f65ec8c34c0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f65ec8c3550>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f65ec8be8a0>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {},
23
+ "observation_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "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",
26
+ "dtype": "float32",
27
+ "_shape": [
28
+ 8
29
+ ],
30
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
31
+ "high": "[inf inf inf inf inf inf inf inf]",
32
+ "bounded_below": "[False False False False False False False False]",
33
+ "bounded_above": "[False False False False False False False False]",
34
+ "_np_random": null
35
+ },
36
+ "action_space": {
37
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
38
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
39
+ "n": 4,
40
+ "_shape": [],
41
+ "dtype": "int64",
42
+ "_np_random": null
43
+ },
44
+ "n_envs": 16,
45
+ "num_timesteps": 1015808,
46
+ "_total_timesteps": 1000000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1672148170717610015,
51
+ "learning_rate": 0.0003,
52
+ "tensorboard_log": null,
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "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"
56
+ },
57
+ "_last_obs": {
58
+ ":type:": "<class 'numpy.ndarray'>",
59
+ ":serialized:": "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"
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.015808000000000044,
70
+ "ep_info_buffer": {
71
+ ":type:": "<class 'collections.deque'>",
72
+ ":serialized:": "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"
73
+ },
74
+ "ep_success_buffer": {
75
+ ":type:": "<class 'collections.deque'>",
76
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
77
+ },
78
+ "_n_updates": 248,
79
+ "n_steps": 1024,
80
+ "gamma": 0.999,
81
+ "gae_lambda": 0.98,
82
+ "ent_coef": 0.01,
83
+ "vf_coef": 0.5,
84
+ "max_grad_norm": 0.5,
85
+ "batch_size": 64,
86
+ "n_epochs": 4,
87
+ "clip_range": {
88
+ ":type:": "<class 'function'>",
89
+ ":serialized:": "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"
90
+ },
91
+ "clip_range_vf": null,
92
+ "normalize_advantage": true,
93
+ "target_kl": null
94
+ }
fire-ostrich/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6084cff39e9d19786129b1251a3a0a5013d5d3679f4ea0f0536b6d2b15ed077d
3
+ size 87929
fire-ostrich/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:941e081c12f8752af17f302ead6cca6ad7ed6e4a973ace6f38203974ac5bfd23
3
+ size 43201
fire-ostrich/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
fire-ostrich/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
2
+ Python: 3.8.16
3
+ Stable-Baselines3: 1.6.2
4
+ PyTorch: 1.13.0+cu116
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
replay.mp4 ADDED
Binary file (223 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 250.40840313060326, "std_reward": 14.201139087643444, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-27T14:04:19.937308"}