Initial commit
Browse files- .gitattributes +1 -0
- README.md +36 -0
- config.json +1 -0
- ppo-Walker2DBulletEnv-v0.zip +3 -0
- ppo-Walker2DBulletEnv-v0/_stable_baselines3_version +1 -0
- ppo-Walker2DBulletEnv-v0/data +120 -0
- ppo-Walker2DBulletEnv-v0/policy.optimizer.pth +3 -0
- ppo-Walker2DBulletEnv-v0/policy.pth +3 -0
- ppo-Walker2DBulletEnv-v0/pytorch_variables.pth +3 -0
- ppo-Walker2DBulletEnv-v0/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
- vec_normalize.pkl +0 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
replay.mp4 filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- Walker2DBulletEnv-v0
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: PPO
|
10 |
+
results:
|
11 |
+
- metrics:
|
12 |
+
- type: mean_reward
|
13 |
+
value: 2426.70 +/- 17.01
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: Walker2DBulletEnv-v0
|
20 |
+
type: Walker2DBulletEnv-v0
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **Walker2DBulletEnv-v0**
|
24 |
+
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
|
25 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
26 |
+
|
27 |
+
## Usage (with Stable-baselines3)
|
28 |
+
TODO: Add your code
|
29 |
+
|
30 |
+
|
31 |
+
```python
|
32 |
+
from stable_baselines3 import ...
|
33 |
+
from huggingface_sb3 import load_from_hub
|
34 |
+
|
35 |
+
...
|
36 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7fa6c09465f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa6c0946680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa6c0946710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa6c09467a0>", "_build": "<function ActorCriticPolicy._build at 0x7fa6c0946830>", "forward": "<function ActorCriticPolicy.forward at 0x7fa6c09468c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa6c0946950>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa6c09469e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa6c0946a70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa6c0946b00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa6c0946b90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa6c0993930>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVjgAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA1hY3RpdmF0aW9uX2ZulIwbdG9yY2gubm4ubW9kdWxlcy5hY3RpdmF0aW9ulIwEUmVMVZSTlIwIbmV0X2FyY2iUXZR9lCiMAnBplF2UKE0AAU0AAWWMAnZmlF2UKE0AAU0AAWV1YXUu", "log_std_init": -2, "ortho_init": false, "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>", "net_arch": [{"pi": [256, 256], "vf": [256, 256]}]}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [22], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -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]", "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]", "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]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [6], "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True]", "bounded_above": "[ True True True True True True]", "_np_random": null}, "n_envs": 16, "num_timesteps": 2007040, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1657928229.7326145, "learning_rate": 3e-05, "tensorboard_log": "./tensorboard", "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": 4, "_current_progress_remaining": -0.0035199999999999676, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 4900, "n_steps": 512, "gamma": 0.99, "gae_lambda": 0.92, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 20, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-Walker2DBulletEnv-v0.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:49224f3d9eaa6666d70848eb05079f38de9a0ce77645d99ded3b3e36868d67ea
|
3 |
+
size 1794903
|
ppo-Walker2DBulletEnv-v0/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
1 |
+
1.6.0
|
ppo-Walker2DBulletEnv-v0/data
ADDED
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7fa6c09465f0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa6c0946680>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa6c0946710>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa6c09467a0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fa6c0946830>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fa6c09468c0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa6c0946950>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fa6c09469e0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa6c0946a70>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa6c0946b00>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa6c0946b90>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fa6c0993930>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {
|
23 |
+
":type:": "<class 'dict'>",
|
24 |
+
":serialized:": "gASVjgAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA1hY3RpdmF0aW9uX2ZulIwbdG9yY2gubm4ubW9kdWxlcy5hY3RpdmF0aW9ulIwEUmVMVZSTlIwIbmV0X2FyY2iUXZR9lCiMAnBplF2UKE0AAU0AAWWMAnZmlF2UKE0AAU0AAWV1YXUu",
|
25 |
+
"log_std_init": -2,
|
26 |
+
"ortho_init": false,
|
27 |
+
"activation_fn": "<class 'torch.nn.modules.activation.ReLU'>",
|
28 |
+
"net_arch": [
|
29 |
+
{
|
30 |
+
"pi": [
|
31 |
+
256,
|
32 |
+
256
|
33 |
+
],
|
34 |
+
"vf": [
|
35 |
+
256,
|
36 |
+
256
|
37 |
+
]
|
38 |
+
}
|
39 |
+
]
|
40 |
+
},
|
41 |
+
"observation_space": {
|
42 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
43 |
+
":serialized:": "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",
|
44 |
+
"dtype": "float32",
|
45 |
+
"_shape": [
|
46 |
+
22
|
47 |
+
],
|
48 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf]",
|
49 |
+
"high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf]",
|
50 |
+
"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]",
|
51 |
+
"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]",
|
52 |
+
"_np_random": null
|
53 |
+
},
|
54 |
+
"action_space": {
|
55 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
56 |
+
":serialized:": "gASVrwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLBoWUjANsb3eUjBVudW1weS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5RoBowHbmRhcnJheZSTlEsAhZRDAWKUh5RSlChLAUsGhZRoColDGAAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5R0lGKMBGhpZ2iUaBJoFEsAhZRoFoeUUpQoSwFLBoWUaAqJQxgAAIA/AACAPwAAgD8AAIA/AACAPwAAgD+UdJRijA1ib3VuZGVkX2JlbG93lGgSaBRLAIWUaBaHlFKUKEsBSwaFlGgHjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiiUMGAQEBAQEBlHSUYowNYm91bmRlZF9hYm92ZZRoEmgUSwCFlGgWh5RSlChLAUsGhZRoKolDBgEBAQEBAZR0lGKMCl9ucF9yYW5kb22UTnViLg==",
|
57 |
+
"dtype": "float32",
|
58 |
+
"_shape": [
|
59 |
+
6
|
60 |
+
],
|
61 |
+
"low": "[-1. -1. -1. -1. -1. -1.]",
|
62 |
+
"high": "[1. 1. 1. 1. 1. 1.]",
|
63 |
+
"bounded_below": "[ True True True True True True]",
|
64 |
+
"bounded_above": "[ True True True True True True]",
|
65 |
+
"_np_random": null
|
66 |
+
},
|
67 |
+
"n_envs": 16,
|
68 |
+
"num_timesteps": 2007040,
|
69 |
+
"_total_timesteps": 2000000,
|
70 |
+
"_num_timesteps_at_start": 0,
|
71 |
+
"seed": null,
|
72 |
+
"action_noise": null,
|
73 |
+
"start_time": 1657928229.7326145,
|
74 |
+
"learning_rate": 3e-05,
|
75 |
+
"tensorboard_log": "./tensorboard",
|
76 |
+
"lr_schedule": {
|
77 |
+
":type:": "<class 'function'>",
|
78 |
+
":serialized:": "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"
|
79 |
+
},
|
80 |
+
"_last_obs": {
|
81 |
+
":type:": "<class 'numpy.ndarray'>",
|
82 |
+
":serialized:": "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"
|
83 |
+
},
|
84 |
+
"_last_episode_starts": {
|
85 |
+
":type:": "<class 'numpy.ndarray'>",
|
86 |
+
":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
|
87 |
+
},
|
88 |
+
"_last_original_obs": {
|
89 |
+
":type:": "<class 'numpy.ndarray'>",
|
90 |
+
":serialized:": "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"
|
91 |
+
},
|
92 |
+
"_episode_num": 0,
|
93 |
+
"use_sde": true,
|
94 |
+
"sde_sample_freq": 4,
|
95 |
+
"_current_progress_remaining": -0.0035199999999999676,
|
96 |
+
"ep_info_buffer": {
|
97 |
+
":type:": "<class 'collections.deque'>",
|
98 |
+
":serialized:": "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"
|
99 |
+
},
|
100 |
+
"ep_success_buffer": {
|
101 |
+
":type:": "<class 'collections.deque'>",
|
102 |
+
":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
103 |
+
},
|
104 |
+
"_n_updates": 4900,
|
105 |
+
"n_steps": 512,
|
106 |
+
"gamma": 0.99,
|
107 |
+
"gae_lambda": 0.92,
|
108 |
+
"ent_coef": 0.0,
|
109 |
+
"vf_coef": 0.5,
|
110 |
+
"max_grad_norm": 0.5,
|
111 |
+
"batch_size": 128,
|
112 |
+
"n_epochs": 20,
|
113 |
+
"clip_range": {
|
114 |
+
":type:": "<class 'function'>",
|
115 |
+
":serialized:": "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"
|
116 |
+
},
|
117 |
+
"clip_range_vf": null,
|
118 |
+
"normalize_advantage": true,
|
119 |
+
"target_kl": null
|
120 |
+
}
|
ppo-Walker2DBulletEnv-v0/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:46a1c11f611a9f13be7c940bd02e323c36ac26c9b9fa5a7cf39e9982a73581f3
|
3 |
+
size 1183984
|
ppo-Walker2DBulletEnv-v0/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:580ebf0afc5b27fa0b7ec82f7a9521bb93fe2fe7536450d13ab5322960b76cce
|
3 |
+
size 591102
|
ppo-Walker2DBulletEnv-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
|
ppo-Walker2DBulletEnv-v0/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
2 |
+
Python: 3.7.13
|
3 |
+
Stable-Baselines3: 1.6.0
|
4 |
+
PyTorch: 1.12.0+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0db1be585bb8905fa1a609e5e07aa4f3fc8733329015b210b84af466cb753e7e
|
3 |
+
size 1092387
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
1 |
+
{"mean_reward": 2426.700848333165, "std_reward": 17.01409113378219, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-16T00:38:43.081412"}
|
vec_normalize.pkl
ADDED
Binary file (3.6 kB). View file
|