brahamdp commited on
Commit
c589d19
1 Parent(s): 3e77028

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: 1558.88 +/- 111.65
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 sha256:87303cf5ef12a18f7db8a3d40002fc3b92d37443508ef1d517c5e77208d82cb0
3
+ size 129265
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 0x7f5a8305c940>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5a8305c9d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5a8305ca60>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5a8305caf0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f5a8305cb80>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f5a8305cc10>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5a8305cca0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5a8305cd30>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f5a8305cdc0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5a8305ce50>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5a8305cee0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5a8305cf70>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7f5a8305df80>"
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": 1679047672559027957,
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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJTDC8e0XxiMAWyUTegDjAF0lEdArJEEMXrMT3V9lChoBkdAmISVJcxCY2gHTegDaAhHQKyWreQdS2p1fZQoaAZHQJmeeCSRr8BoB03oA2gIR0Csm3qkM1CPdX2UKGgGR0CXajc580DVaAdN6ANoCEdArJxVKf4AS3V9lChoBkdAmpT1ocrAg2gHTegDaAhHQKydP/Q0GeN1fZQoaAZHQJNBS5CngpBoB03oA2gIR0CsouaQ3gk1dX2UKGgGR0CZhd08NhE0aAdN6ANoCEdArKo5aouPFXV9lChoBkdAmCeN4JNTLmgHTegDaAhHQKyrkOlwcYJ1fZQoaAZHQJn2xeqrBCVoB03oA2gIR0CsrQM4cWCVdX2UKGgGR0CYqm74BV+7aAdN6ANoCEdArLL8XenAI3V9lChoBkdAmN5tHDrJKmgHTegDaAhHQKy3pKcNH6N1fZQoaAZHQJfVg7zTWoZoB03oA2gIR0CsuHR4IKMOdX2UKGgGR0CZO76DGtITaAdN6ANoCEdArLlcx9G7SXV9lChoBkdAmjiTrAxi5WgHTegDaAhHQKy+3AMUh3d1fZQoaAZHQJtzFjiGWUtoB03oA2gIR0CsxIGXPZ7HdX2UKGgGR0CUXv44Ia99aAdN6ANoCEdArMXs94eLenV9lChoBkdAl8pytFKChGgHTegDaAhHQKzHV/4Irvt1fZQoaAZHQJfWQm9g4OtoB03oA2gIR0CszzxCx/utdX2UKGgGR0Caz7jHGS6laAdN6ANoCEdArNPb+PzWgHV9lChoBkdAnGcbv1DjR2gHTegDaAhHQKzUr+EytV91fZQoaAZHQJnkN9ORDCxoB03oA2gIR0Cs1ZCMHbAUdX2UKGgGR0CW99TCcf/4aAdN6ANoCEdArNsz7uUliXV9lChoBkdAmHN3NC7btmgHTegDaAhHQKzfzR8c+7l1fZQoaAZHQJrfyfh/Aj9oB03oA2gIR0Cs4Kl3IMjNdX2UKGgGR0CaQcmAskIHaAdN6ANoCEdArOG8l7dBSnV9lChoBkdAlzvgA+6iCmgHTegDaAhHQKzqrTrmhdt1fZQoaAZHQJ0BjKxLTQVoB03oA2gIR0Cs8AG+bmU4dX2UKGgGR0Cbz94t6HCXaAdN6ANoCEdArPDXVsk6cXV9lChoBkdAm68qMWGh3GgHTegDaAhHQKzxuN4qwyJ1fZQoaAZHQJeZ8/8l5W1oB03oA2gIR0Cs92fReC04dX2UKGgGR0CZblDHOryUaAdN6ANoCEdArPwiEOAiFHV9lChoBkdAm0w+Y6XBxmgHTegDaAhHQKz89C2tuDV1fZQoaAZHQJo6oTYdyT9oB03oA2gIR0Cs/dwrc0tRdX2UKGgGR0Ca5zX2M85kaAdN6ANoCEdArQWfNNahYnV9lChoBkdAmYLXu3MINWgHTegDaAhHQK0MnM/yGzt1fZQoaAZHQJJO5xJd0JZoB03oA2gIR0CtDXRkNFz/dX2UKGgGR0CZFtG0/nnuaAdN6ANoCEdArQ5ctGus93V9lChoBkdAmHTk9IPK+2gHTegDaAhHQK0UE5vtMPB1fZQoaAZHQJoQcG1QZXNoB03oA2gIR0CtGOG6f8MvdX2UKGgGR0CZkY6RQrMDaAdN6ANoCEdArRm38ZUDMnV9lChoBkdAkNrZEH+qBGgHTegDaAhHQK0ak0l7dBV1fZQoaAZHQJixz7Q9ic5oB03oA2gIR0CtIJ3r+o9+dX2UKGgGR0CZVOycTakAaAdN6ANoCEdArSgCa7VawHV9lChoBkdAmvqd8Z1mrmgHTegDaAhHQK0pXTMqz7d1fZQoaAZHQJu55nezlcRoB03oA2gIR0CtKrSnUDuCdX2UKGgGR0Cax07Z39rHaAdN6ANoCEdArTBfU+cH4XV9lChoBkdAnBOW9g4OtmgHTegDaAhHQK01HU6xPft1fZQoaAZHQJjxM/W1+iJoB03oA2gIR0CtNfMl9jPOdX2UKGgGR0Cc5ph4dIXkaAdN6ANoCEdArTbMTakAP3V9lChoBkdAm3OeyNXHR2gHTegDaAhHQK08dIOpbUx1fZQoaAZHQJkjVjG1hLJoB03oA2gIR0CtQq6IN3GGdX2UKGgGR0CTyucn3L3caAdN6ANoCEdArUQM2vStvHV9lChoBkdAl1EpeVs1sWgHTegDaAhHQK1Fg/sVtXR1fZQoaAZHQJZ9lShrWRRoB03oA2gIR0CtTL500WM1dX2UKGgGR0CaF66Mzdk8aAdN6ANoCEdArVF8SqU/wHV9lChoBkdAmNtTxG2CumgHTegDaAhHQK1STv7WNFV1fZQoaAZHQJutMgow22poB03oA2gIR0CtUyljVhCudX2UKGgGR0CbpCum78NyaAdN6ANoCEdArViycslLOHV9lChoBkdAnPaLOmixmmgHTegDaAhHQK1dReE7GNt1fZQoaAZHQJxr1m5DqnpoB03oA2gIR0CtXlKG+K0ldX2UKGgGR0CbJ8Wom5UcaAdN6ANoCEdArV+c41gpjXV9lChoBkdAnFC7z06HTWgHTegDaAhHQK1oh1VYISl1fZQoaAZHQJyKeifxtpFoB03oA2gIR0CtbWZYYBNmdX2UKGgGR0CbaDiADq4ZaAdN6ANoCEdArW48cp9ZzXV9lChoBkdAnUR3Onl4kmgHTegDaAhHQK1vFb212JV1fZQoaAZHQJr4NXKbKA9oB03oA2gIR0CtdL5ULlV+dX2UKGgGR0CbUV33YcvNaAdN6ANoCEdArXmGce8wpXV9lChoBkdAnDtIDTz/ZWgHTegDaAhHQK16Ynrpqyp1fZQoaAZHQJngzH3lCC1oB03oA2gIR0Cte1QiRnvldX2UKGgGR0CdsNhzNliCaAdN6ANoCEdArYNp2+wkgXV9lChoBkdAm1A8CLdepmgHTegDaAhHQK2J2HzH0bt1fZQoaAZHQJ1m5IsiB5JoB03oA2gIR0CtiqdBjWkKdX2UKGgGR0CejIV1wHZ9aAdN6ANoCEdArYuNx4ptrXV9lChoBkdAnWTfLgXMyWgHTegDaAhHQK2RLJgb6xh1fZQoaAZHQJ3bS/h2nsNoB03oA2gIR0Ctld7G3nZCdX2UKGgGR0CZDPfg75mAaAdN6ANoCEdArZasqJ/G2nV9lChoBkdAnlSkk8ifQWgHTegDaAhHQK2Xj4A0bcZ1fZQoaAZHQJugLqJMxoJoB03oA2gIR0CtnfkDQqqfdX2UKGgGR0Cc4PmEXcgyaAdN6ANoCEdAraWGICU5dXV9lChoBkdAlSWrO7g882gHTegDaAhHQK2m5I/Z/Td1fZQoaAZHQJvFvsAvL5hoB03oA2gIR0Ctp8bJ4jbBdX2UKGgGR0CbIBf4h2W6aAdN6ANoCEdAra135eqrBHV9lChoBkdAmUf/bblA/2gHTegDaAhHQK2yLFaSs8x1fZQoaAZHQJqIgVSGahJoB03oA2gIR0Ctsvi04R29dX2UKGgGR0Cb4YkJ8fFKaAdN6ANoCEdArbPZWo3rEHV9lChoBkdAmqYMGkep42gHTegDaAhHQK25jfJFLFp1fZQoaAZHQJutyI55qudoB03oA2gIR0CtwBUjkdWAdX2UKGgGR0CaWFc94eLfaAdN6ANoCEdArcFvZbpu/HV9lChoBkdAmzQl9BrvcGgHTegDaAhHQK3C28cuJ1t1fZQoaAZHQJp1t/jKgZloB03oA2gIR0CtycRjJ+2FdX2UKGgGR0CZOOWDpTuOaAdN6ANoCEdArc6Udkrf+HV9lChoBkdAlMZ/ZqVQh2gHTegDaAhHQK3PcOAiFCd1fZQoaAZHQJiRUBcRlH1oB03oA2gIR0Ct0FCFj/dZdX2UKGgGR0CWZhR15jYqaAdN6ANoCEdArdYDyYoiLXV9lChoBkdAlv71L39JjGgHTegDaAhHQK3bbe0ojOd1fZQoaAZHQJazawosqaxoB03oA2gIR0Ct3K5ntfG/dX2UKGgGR0CYnlQnQY1paAdN6ANoCEdArd4TD0lJH3V9lChoBkdAlK73PeHi32gHTegDaAhHQK3mmIpH7P91fZQoaAZHQJhAwvAXVLBoB03oA2gIR0Ct62i17Y03dX2UKGgGR0CU/JXwb2lEaAdN6ANoCEdArexHWYnfEXVlLg=="
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 sha256:15c1e24b60711689a22343569f3572e0f9c618f73b4cc3672ece7a04ea6b8e5e
3
+ size 56190
a2c-AntBulletEnv-v0/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1322816dbcab6823b4719f707a6539af83d4150cfc9dc8a675402b76f64ac8f5
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.9.16
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 0x7f5a8305c940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5a8305c9d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5a8305ca60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5a8305caf0>", "_build": "<function ActorCriticPolicy._build at 0x7f5a8305cb80>", "forward": "<function ActorCriticPolicy.forward at 0x7f5a8305cc10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5a8305cca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5a8305cd30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5a8305cdc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5a8305ce50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5a8305cee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5a8305cf70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5a8305df80>"}, "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": 1679047672559027957, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAJ7MeEAK4aO+4Yr3PhWlpL/1/Ya+Y2qzv06REj+D1Jg/3J+dv7yrzT/osjfAOGWAvH2tJr/N7Ce/aTqyP4W6l8DO8qi/2SEkvpholD8i6VLAUyItP99dGr8wBCLAqdItPxwxlL8Ob/m/AQTtv6wImL8e5ZO+hCMpvnd/Bj9gip48zbwYvqgxCD+YidA9pvvmvpAxWb9v4IA/jwM9vvIuzT9OwlW/FIEbv9x4ZD68V56/VqWIP7E3tr7/hIM+oFlWPgSd674gUyk/0hwSv9/L4L8cMZS/mV4DP6JACj+sCJi/ehEFv+Luv7/N2xq/rwdQPWHBu7/mBis+jQcHP8vbfr5IJqM+LEyCP7y31D66w5O+hAmEv9Daxz5x9Iy9x3viPmnCCT9YJRM+FnJEP5108j7WqNK9f+5lPzqWSL+ZjmE/aR5dP5leAz+iQAo/rAiYv1kKlb0utdg98rcNP4baHz8UWn6/TpxJvxm5R71jq46/OekIP+Bakr3TJsc+XYGMv99OOL+2gdY+C8XxPrYyVD9WcPk+2JMFP5VXSD9SzpG8F8v4vr0Vkb+iYoc+3CoqQBwxlL+ZXgM/AQTtv6wImL+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (976 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 1558.8820664568805, "std_reward": 111.65499356155605, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-17T11:29:09.616569"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:607149e566351a274885d88615e1401f70fca93f510d6d1c77974c77ea6d397a
3
+ size 2136