thuyentruong commited on
Commit
7961aa3
1 Parent(s): 5a4e829

Initial commit

Browse files
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: 1741.60 +/- 107.57
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:cfd9fb70980971fbcedd6345f05c3d489611c205e296d604127ce4ce0dcf2b41
3
+ size 129248
a2c-AntBulletEnv-v0/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.8.0
a2c-AntBulletEnv-v0/data ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7f7257c8e710>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7257c8e7a0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7257c8e830>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7257c8e8c0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f7257c8e950>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f7257c8e9e0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7257c8ea70>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7257c8eb00>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f7257c8eb90>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7257c8ec20>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7257c8ecb0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7257c8ed40>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7f7257c83400>"
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
+ "num_timesteps": 2000000,
36
+ "_total_timesteps": 2000000,
37
+ "_num_timesteps_at_start": 0,
38
+ "seed": null,
39
+ "action_noise": null,
40
+ "start_time": 1683613858707472663,
41
+ "learning_rate": 0.00096,
42
+ "tensorboard_log": null,
43
+ "lr_schedule": {
44
+ ":type:": "<class 'function'>",
45
+ ":serialized:": "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"
46
+ },
47
+ "_last_obs": {
48
+ ":type:": "<class 'numpy.ndarray'>",
49
+ ":serialized:": "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"
50
+ },
51
+ "_last_episode_starts": {
52
+ ":type:": "<class 'numpy.ndarray'>",
53
+ ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
54
+ },
55
+ "_last_original_obs": {
56
+ ":type:": "<class 'numpy.ndarray'>",
57
+ ":serialized:": "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"
58
+ },
59
+ "_episode_num": 0,
60
+ "use_sde": true,
61
+ "sde_sample_freq": -1,
62
+ "_current_progress_remaining": 0.0,
63
+ "_stats_window_size": 100,
64
+ "ep_info_buffer": {
65
+ ":type:": "<class 'collections.deque'>",
66
+ ":serialized:": "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"
67
+ },
68
+ "ep_success_buffer": {
69
+ ":type:": "<class 'collections.deque'>",
70
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
71
+ },
72
+ "_n_updates": 62500,
73
+ "n_steps": 8,
74
+ "gamma": 0.99,
75
+ "gae_lambda": 0.9,
76
+ "ent_coef": 0.0,
77
+ "vf_coef": 0.4,
78
+ "max_grad_norm": 0.5,
79
+ "normalize_advantage": false,
80
+ "observation_space": {
81
+ ":type:": "<class 'gym.spaces.box.Box'>",
82
+ ":serialized:": "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",
83
+ "dtype": "float32",
84
+ "_shape": [
85
+ 28
86
+ ],
87
+ "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]",
88
+ "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]",
89
+ "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]",
90
+ "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]",
91
+ "_np_random": null
92
+ },
93
+ "action_space": {
94
+ ":type:": "<class 'gym.spaces.box.Box'>",
95
+ ":serialized:": "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",
96
+ "dtype": "float32",
97
+ "_shape": [
98
+ 8
99
+ ],
100
+ "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
101
+ "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
102
+ "bounded_below": "[ True True True True True True True True]",
103
+ "bounded_above": "[ True True True True True True True True]",
104
+ "_np_random": null
105
+ },
106
+ "n_envs": 4
107
+ }
a2c-AntBulletEnv-v0/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:748109d5e9d35d3f66183eab58f75cfec74dd9d10c6f2e200ca04825169cbb7a
3
+ size 56190
a2c-AntBulletEnv-v0/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5d4eb140f3d9f924dc74073df1631cf603a9604484b1bd9701724efa2f6cb091
3
+ size 56894
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.10.11
3
+ - Stable-Baselines3: 1.8.0
4
+ - PyTorch: 2.0.0+cu118
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 0x7f7257c8e710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7257c8e7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7257c8e830>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7257c8e8c0>", "_build": "<function ActorCriticPolicy._build at 0x7f7257c8e950>", "forward": "<function ActorCriticPolicy.forward at 0x7f7257c8e9e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7257c8ea70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7257c8eb00>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7257c8eb90>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7257c8ec20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7257c8ecb0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7257c8ed40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7257c83400>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683613858707472663, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9PdRBNVR1phZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (965 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 1741.6014753481372, "std_reward": 107.57369113526651, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-09T07:30:07.059436"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:288c22a3cf75187751beb05e00064fce8fe79dd0c2e68b8726fbf4bbea31c2c0
3
+ size 2176