stinoco commited on
Commit
e3c3896
1 Parent(s): 4278747

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: 1786.76 +/- 84.87
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 sha256:08f235670c32988d981f3e5aee4408cd6b801108c0b26d11b16b67aeec30c448
3
+ size 129256
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 0x7effd6062820>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7effd60628b0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7effd6062940>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7effd60629d0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7effd6062a60>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7effd6062af0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7effd6062b80>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7effd6062c10>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7effd6062ca0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7effd6062d30>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7effd6062dc0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7effd6062e50>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7effd605db70>"
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": 1677612431044423315,
68
+ "learning_rate": 0.00096,
69
+ "tensorboard_log": null,
70
+ "lr_schedule": {
71
+ ":type:": "<class 'function'>",
72
+ ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
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:e9775cc0624429097d75025fca89c7a2edb0abfb3a8efbb5f1e4f6c1bb881d04
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:ed03f4a5661b88f620ef516af4fd32bb621045c70871d48bf5a3cf387764b881
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 0x7effd6062820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7effd60628b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7effd6062940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7effd60629d0>", "_build": "<function ActorCriticPolicy._build at 0x7effd6062a60>", "forward": "<function ActorCriticPolicy.forward at 0x7effd6062af0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7effd6062b80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7effd6062c10>", "_predict": "<function ActorCriticPolicy._predict at 0x7effd6062ca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7effd6062d30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7effd6062dc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7effd6062e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7effd605db70>"}, "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": 1677612431044423315, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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 sha256:6ef3612de6a766d181b013ea67994ac3fd2269aa8e2166a250a525724e29aa53
3
+ size 1023688
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 1786.7570644832783, "std_reward": 84.86836253129516, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T20:37:08.181478"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f7bca803316b8f18b105cb83eff91e531b5ca18917df703161f17f085ac1e134
3
+ size 2136