JessicaHsu commited on
Commit
a69b199
1 Parent(s): b89fd2b

Initial commit

Browse files
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
32
  *.zip filter=lfs diff=lfs merge=lfs -text
33
  *.zst filter=lfs diff=lfs merge=lfs -text
34
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
32
  *.zip filter=lfs diff=lfs merge=lfs -text
33
  *.zst filter=lfs diff=lfs merge=lfs -text
34
  *tfevents* filter=lfs diff=lfs merge=lfs -text
35
+ replay.mp4 filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - AntBulletEnv-v0
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: A2C
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: AntBulletEnv-v0
16
+ type: AntBulletEnv-v0
17
+ metrics:
18
+ - type: mean_reward
19
+ value: 1897.90 +/- 192.91
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **A2C** Agent playing **AntBulletEnv-v0**
25
+ This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
+ ```
a2c-AntBulletEnv-v0.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:627d9c51b5af0deaa307ee661c6fedba5d716b523256d8e7978462b5b87babe5
3
+ size 129260
a2c-AntBulletEnv-v0/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
a2c-AntBulletEnv-v0/data ADDED
@@ -0,0 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7f6bad869940>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6bad8699d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6bad869a60>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6bad869af0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f6bad869b80>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f6bad869c10>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6bad869ca0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6bad869d30>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f6bad869dc0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6bad869e50>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6bad869ee0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6bad869f70>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f6bad866390>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {
24
+ ":type:": "<class 'dict'>",
25
+ ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
26
+ "log_std_init": -2,
27
+ "ortho_init": false,
28
+ "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
29
+ "optimizer_kwargs": {
30
+ "alpha": 0.99,
31
+ "eps": 1e-05,
32
+ "weight_decay": 0
33
+ }
34
+ },
35
+ "observation_space": {
36
+ ":type:": "<class 'gym.spaces.box.Box'>",
37
+ ":serialized:": "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",
38
+ "dtype": "float32",
39
+ "_shape": [
40
+ 28
41
+ ],
42
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
43
+ "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
44
+ "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
45
+ "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
46
+ "_np_random": null
47
+ },
48
+ "action_space": {
49
+ ":type:": "<class 'gym.spaces.box.Box'>",
50
+ ":serialized:": "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",
51
+ "dtype": "float32",
52
+ "_shape": [
53
+ 8
54
+ ],
55
+ "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
56
+ "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
57
+ "bounded_below": "[ True True True True True True True True]",
58
+ "bounded_above": "[ True True True True True True True True]",
59
+ "_np_random": null
60
+ },
61
+ "n_envs": 4,
62
+ "num_timesteps": 2000000,
63
+ "_total_timesteps": 2000000,
64
+ "_num_timesteps_at_start": 0,
65
+ "seed": null,
66
+ "action_noise": null,
67
+ "start_time": 1678255994506812366,
68
+ "learning_rate": 0.00096,
69
+ "tensorboard_log": null,
70
+ "lr_schedule": {
71
+ ":type:": "<class 'function'>",
72
+ ":serialized:": "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"
73
+ },
74
+ "_last_obs": {
75
+ ":type:": "<class 'numpy.ndarray'>",
76
+ ":serialized:": "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"
77
+ },
78
+ "_last_episode_starts": {
79
+ ":type:": "<class 'numpy.ndarray'>",
80
+ ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
81
+ },
82
+ "_last_original_obs": {
83
+ ":type:": "<class 'numpy.ndarray'>",
84
+ ":serialized:": "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"
85
+ },
86
+ "_episode_num": 0,
87
+ "use_sde": true,
88
+ "sde_sample_freq": -1,
89
+ "_current_progress_remaining": 0.0,
90
+ "ep_info_buffer": {
91
+ ":type:": "<class 'collections.deque'>",
92
+ ":serialized:": "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"
93
+ },
94
+ "ep_success_buffer": {
95
+ ":type:": "<class 'collections.deque'>",
96
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
97
+ },
98
+ "_n_updates": 62500,
99
+ "n_steps": 8,
100
+ "gamma": 0.99,
101
+ "gae_lambda": 0.9,
102
+ "ent_coef": 0.0,
103
+ "vf_coef": 0.4,
104
+ "max_grad_norm": 0.5,
105
+ "normalize_advantage": false
106
+ }
a2c-AntBulletEnv-v0/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7dcde89bc4e8f98f472bf7d34298c466ee7c9f37e0c41d98f87ae02d54c780ef
3
+ size 56190
a2c-AntBulletEnv-v0/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8000df8f72816559396bf4bb38c946b37e9a9854092d7a59353950fca9a6d443
3
+ size 56958
a2c-AntBulletEnv-v0/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
a2c-AntBulletEnv-v0/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: 1.22.4
7
+ - Gym: 0.21.0
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 0x7f6bad869940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6bad8699d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6bad869a60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6bad869af0>", "_build": "<function ActorCriticPolicy._build at 0x7f6bad869b80>", "forward": "<function ActorCriticPolicy.forward at 0x7f6bad869c10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6bad869ca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6bad869d30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6bad869dc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6bad869e50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6bad869ee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6bad869f70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6bad866390>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678255994506812366, "learning_rate": 0.00096, "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "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": "1.22.4", "Gym": "0.21.0"}}
replay.mp4 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:981557d118f0121fb41b9e435936428c18847cbbfae742de92dad4ec8f9e8574
3
+ size 1123396
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 1897.9009037015378, "std_reward": 192.91059809269808, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-08T07:15:42.576071"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5a750227394f018c5e8aa9a10857b0201e372612c8ae41f01b429518931aeb68
3
+ size 2136