Tiru8055 commited on
Commit
11069af
1 Parent(s): a33abc5

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: 1392.82 +/- 237.55
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 sha256:4ddd9756b8b41e48013cdabf91210c181097354675092daa7eb2287021e458df
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 0x7f9adc412830>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9adc4128c0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9adc412950>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9adc4129e0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f9adc412a70>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f9adc412b00>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9adc412b90>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9adc412c20>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f9adc412cb0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9adc412d40>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9adc412dd0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9adc412e60>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7f9a79d6fec0>"
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": 1687406844867959979,
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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAACPoSC2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACA9HGUPQAAAACjxPK/AAAAAAufjD0AAAAAlon6PwAAAABHVrS9AAAAANk13D8AAAAAm+srPAAAAADljuC/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAXWC9tQAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgHzxkj0AAAAAC0DqvwAAAADS+O49AAAAACvt/D8AAAAAs9+7vQAAAADeNeM/AAAAAAKfhTwAAAAAZkLbvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP0Om7YAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAICTc9A9AAAAAFKP3r8AAAAAYCG6uQAAAADao/Q/AAAAADOhADwAAAAAqd7uPwAAAADZI9g9AAAAAD5a7r8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD5tbO2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACA2XjTPQAAAAAJVuy/AAAAAK/8C74AAAAAWfryPwAAAACAtwG+AAAAAGNb+T8AAAAABtgOvgAAAABMQPm/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJZY+x3V092MAWyUTegDjAF0lEdAqsJjDjzZpXV9lChoBkdAmPlxppN9IGgHTegDaAhHQKrDfc+qzZ91fZQoaAZHQJZHyAG0NSZoB03oA2gIR0CqxFhSLqD9dX2UKGgGR0CYncEdNnGsaAdN6ANoCEdAqsffY+Sr53V9lChoBkdAmQxoJeE7GWgHTegDaAhHQKrOpA/LTx51fZQoaAZHQJhquKUFB6doB03oA2gIR0Cqz7wjdHlPdX2UKGgGR0CaTZf0mMOxaAdN6ANoCEdAqtCW5xzaK3V9lChoBkdAmFEVbFCLM2gHTegDaAhHQKrUq4DLbHp1fZQoaAZHQJrxK938n/loB03oA2gIR0Cq3oeGGmDUdX2UKGgGR0CWGTOXmeUZaAdN6ANoCEdAqt+hc/t6X3V9lChoBkdAmK/86eXiSGgHTegDaAhHQKrgesI3R5V1fZQoaAZHQJgL3D2rXDpoB03oA2gIR0Cq4/oJZ4fPdX2UKGgGR0CaXYLW7OE/aAdN6ANoCEdAquqpAOavzXV9lChoBkdAmX3fkzXSSmgHTegDaAhHQKrrx52Qnx91fZQoaAZHQJhol1GLDQ9oB03oA2gIR0Cq7KtNzr/sdX2UKGgGR0CYy0ObRWtEaAdN6ANoCEdAqvA/vfCQ93V9lChoBkdAlE4QKrq+rWgHTegDaAhHQKr5uCvHLid1fZQoaAZHQJYaJUZNwitoB03oA2gIR0Cq+2NCzC1rdX2UKGgGR0CYdGCZF5OaaAdN6ANoCEdAqvzCyKNyYHV9lChoBkdAl/eC1E3KjmgHTegDaAhHQKsATvze41B1fZQoaAZHQJiqhz1bqyJoB03oA2gIR0CrBvnzH0btdX2UKGgGR0CXnvM3qAz6aAdN6ANoCEdAqwgMOuq3mXV9lChoBkdAmKdt/BnBcmgHTegDaAhHQKsI7o7FKkF1fZQoaAZHQJdBhSAH3URoB03oA2gIR0CrDG6zu4PPdX2UKGgGR0CY5FLYf4h2aAdN6ANoCEdAqxQ5GDtgKHV9lChoBkdAluAqR+z+m2gHTegDaAhHQKsV0Jlar3l1fZQoaAZHQJk52w0O3DxoB03oA2gIR0CrFy3L/0dzdX2UKGgGR0CWHSiwSrYHaAdN6ANoCEdAqxwpXU6PsHV9lChoBkdAlzK82itaIWgHTegDaAhHQKsi35C4SYh1fZQoaAZHQJrZ6umrKeVoB03oA2gIR0CrI/NRNyo5dX2UKGgGR0CZ9nwXIlt1aAdN6ANoCEdAqyTPr8iwCHV9lChoBkdAmsS/GlyimGgHTegDaAhHQKsoVsP8Q7N1fZQoaAZHQJcyWfmLcbloB03oA2gIR0CrLwnXEqDsdX2UKGgGR0CY2insLORlaAdN6ANoCEdAqzB1a+vhZXV9lChoBkdAl3mFtO2y9mgHTegDaAhHQKsxyeii7Cl1fZQoaAZHQJZ4vrmhdt5oB03oA2gIR0CrNzozN2TxdX2UKGgGR0CVKAmDUVi4aAdN6ANoCEdAqz82eBg/knV9lChoBkdAls6yyUs4DWgHTegDaAhHQKtAWdlum791fZQoaAZHQJZomd/axotoB03oA2gIR0CrQToKD017dX2UKGgGR0CV2HOzY287aAdN6ANoCEdAq0S9VcUuc3V9lChoBkdAldVTW5H3DmgHTegDaAhHQKtLk/fO2Rd1fZQoaAZHQJWpfV2A5JdoB03oA2gIR0CrTLD7yhBadX2UKGgGR0CTG6nYQJ5WaAdN6ANoCEdAq02avC/Gl3V9lChoBkdAmP8Sro4dZWgHTegDaAhHQKtSb0pVjqh1fZQoaAZHQJigdOtW+49oB03oA2gIR0CrW6Hmig01dX2UKGgGR0CYuOAdGRV7aAdN6ANoCEdAq1y4EOiFkHV9lChoBkdAmfeTSb6P82gHTegDaAhHQKtdm9TxXn11fZQoaAZHQJtAqUcGTs9oB03oA2gIR0CrYTq9f1HwdX2UKGgGR0CbCDmuTzNEaAdN6ANoCEdAq2f3Zdv863V9lChoBkdAnBmSDEm6XmgHTegDaAhHQKtpDf6XSjR1fZQoaAZHQJmxF7CzkZJoB03oA2gIR0CraemWt2cKdX2UKGgGR0Cb3Hsyi22HaAdN6ANoCEdAq22S1y/9HnV9lChoBkdAm9SZCBwuNGgHTegDaAhHQKt3jZDArQR1fZQoaAZHQJy2iJpFkQRoB03oA2gIR0CreLv4mCyydX2UKGgGR0CbYtMDOkckaAdN6ANoCEdAq3misbNr03V9lChoBkdAm9b9S619fGgHTegDaAhHQKt9Hr2QGOd1fZQoaAZHQJ0kUB0ZFXtoB03oA2gIR0Crg9ppWV/udX2UKGgGR0CZkOKSPluFaAdN6ANoCEdAq4TlQ66renV9lChoBkdAnSjl81Gb1GgHTegDaAhHQKuFyrrgOz91fZQoaAZHQJcAeX1J17poB03oA2gIR0CriUYGUwBYdX2UKGgGR0CcNrDBdld1aAdN6ANoCEdAq5HxeXzDoHV9lChoBkdAmkqGXb/OuGgHTegDaAhHQKuTnKTSssB1fZQoaAZHQJuCuprDZUVoB03oA2gIR0CrlPhMSK3vdX2UKGgGR0CbcJ/2Cdz5aAdN6ANoCEdAq5kVHH3lCHV9lChoBkdAnGjITsY2sWgHTegDaAhHQKuf7clgMMJ1fZQoaAZHQJwwpGy5Zr5oB03oA2gIR0CroQfIbOu8dX2UKGgGR0CbfBKqn3tbaAdN6ANoCEdAq6HnoJRfnnV9lChoBkdAnMoJ4W1twmgHTegDaAhHQKuli9IPK+11fZQoaAZHQJ30zpxFRYRoB03oA2gIR0CrrTRkd3jddX2UKGgGR0CV9pOv+wTuaAdN6ANoCEdAq669at9x63V9lChoBkdAneGhk3CKrWgHTegDaAhHQKuwFGR3eN11fZQoaAZHQJ4PYEkjX4FoB03oA2gIR0CrtYV32VVxdX2UKGgGR0Cf2V/n4fwJaAdN6ANoCEdAq7w4FvAGjnV9lChoBkdAnriLPY4ACGgHTegDaAhHQKu9R6O5rgx1fZQoaAZHQJ7B+W2PT5RoB03oA2gIR0CrviXQla8pdX2UKGgGR0CeSXn3cpLFaAdN6ANoCEdAq8GytozvZ3V9lChoBkdAmX1HY150KmgHTegDaAhHQKvIZ4i5d4V1fZQoaAZHQJ28n1lGwzNoB03oA2gIR0CryYyW7e2vdX2UKGgGR0CeaobFS88LaAdN6ANoCEdAq8rPTAnDznV9lChoBkdAlIplsP8Q7WgHTegDaAhHQKvQGPAfuCx1fZQoaAZHQJ6+q+N96TpoB03oA2gIR0Cr2FhrnDBNdX2UKGgGR0CdUCc7yQPqaAdN6ANoCEdAq9lqlDWsinV9lChoBkdAkp4JiuuA7WgHTegDaAhHQKvaR8/lhgF1fZQoaAZHQJ3tRyU9pypoB03oA2gIR0Cr3dHkcS5BdX2UKGgGR0Ccit+eOGTLaAdN6ANoCEdAq+Sm/Firk3V9lChoBkdAminpl8PWhGgHTegDaAhHQKvlvb0voNd1fZQoaAZHQJtH/BP9DQZoB03oA2gIR0Cr5pt7rs0IdX2UKGgGR0CejAQgs9SuaAdN6ANoCEdAq+r0AHVwxXV9lChoBkdAmnrSWmgrY2gHTegDaAhHQKv0kzMzMzN1fZQoaAZHQJy6kj9n9NxoB03oA2gIR0Cr9adlmOENdX2UKGgGR0Ce2DpKjBVNaAdN6ANoCEdAq/aMqOLiuXV9lChoBkdAnMjGIXTEzmgHTegDaAhHQKv6HOUMXrN1fZQoaAZHQJ1tB7Z39rJoB03oA2gIR0CsAOCl7+kydX2UKGgGR0Cap8hC+lCUaAdN6ANoCEdArAHxZ0Syt3V9lChoBkdAmv1BwhnrZGgHTegDaAhHQKwC2YHgP3B1fZQoaAZHQJyiaSNfgJloB03oA2gIR0CsBmHI6r/9dX2UKGgGR0CdHShmXgLraAdN6ANoCEdArBAad6LOzXV9lChoBkdAmtp0nb7CSGgHTegDaAhHQKwRy5myxA11fZQoaAZHQJ3OfoHLRrtoB03oA2gIR0CsEsBvrGBGdX2UKGgGR0CbAvQlKK51aAdN6ANoCEdArBZZVU+9rXVlLg=="
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 sha256:068f3d5eea6ae4f3e81bbf251fe8a8e26ac62bd222c3f207a33104d1ec95a702
3
+ size 56190
a2c-AntBulletEnv-v0/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3ef4818319ce974f3c9bdc024c914aefed7550cf37ddcf4cfe5cd47f54437418
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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
2
+ - Python: 3.10.12
3
+ - Stable-Baselines3: 1.8.0
4
+ - PyTorch: 2.0.1+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 0x7f9adc412830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9adc4128c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9adc412950>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9adc4129e0>", "_build": "<function ActorCriticPolicy._build at 0x7f9adc412a70>", "forward": "<function ActorCriticPolicy.forward at 0x7f9adc412b00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9adc412b90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9adc412c20>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9adc412cb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9adc412d40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9adc412dd0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9adc412e60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9a79d6fec0>"}, "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": 1687406844867959979, "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAACPoSC2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACA9HGUPQAAAACjxPK/AAAAAAufjD0AAAAAlon6PwAAAABHVrS9AAAAANk13D8AAAAAm+srPAAAAADljuC/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAXWC9tQAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgHzxkj0AAAAAC0DqvwAAAADS+O49AAAAACvt/D8AAAAAs9+7vQAAAADeNeM/AAAAAAKfhTwAAAAAZkLbvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP0Om7YAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAICTc9A9AAAAAFKP3r8AAAAAYCG6uQAAAADao/Q/AAAAADOhADwAAAAAqd7uPwAAAADZI9g9AAAAAD5a7r8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD5tbO2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACA2XjTPQAAAAAJVuy/AAAAAK/8C74AAAAAWfryPwAAAACAtwG+AAAAAGNb+T8AAAAABtgOvgAAAABMQPm/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (964 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 1392.8152419593418, "std_reward": 237.55144991883517, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-22T05:08:42.054829"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:81a21cee6b9e4bab226fa9521beffa0a3f99c1d1a1dbb575e79ccf63d99cad9b
3
+ size 2176