Initial commit
Browse files- .gitattributes +1 -0
- README.md +37 -0
- a2c-AntBulletEnv-v0.zip +3 -0
- a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -0
- a2c-AntBulletEnv-v0/data +106 -0
- a2c-AntBulletEnv-v0/policy.optimizer.pth +3 -0
- a2c-AntBulletEnv-v0/policy.pth +3 -0
- a2c-AntBulletEnv-v0/pytorch_variables.pth +3 -0
- a2c-AntBulletEnv-v0/system_info.txt +7 -0
- config.json +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
.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: 2159.83 +/- 43.32
|
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 sha256:0b8e445b2a42f15722d646ba95af6b07fdf9cf3e25c144e68b95ea3e67b8f9e0
|
3 |
+
size 129260
|
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 0x7f6aea5173a0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6aea517430>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6aea5174c0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6aea517550>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f6aea5175e0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f6aea517670>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6aea517700>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6aea517790>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f6aea517820>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6aea5178b0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6aea517940>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6aea5179d0>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc_data object at 0x7f6aea50eed0>"
|
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": 2200000,
|
63 |
+
"_total_timesteps": 2200000,
|
64 |
+
"_num_timesteps_at_start": 0,
|
65 |
+
"seed": null,
|
66 |
+
"action_noise": null,
|
67 |
+
"start_time": 1674742653733934877,
|
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": 68750,
|
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 sha256:662b45f7f87c052332fda4be001f393e2bcf6fc07b53aadecce56f2fcaf1a362
|
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:a8e67e1f3251d30b9b0d5f0dae10fa32c474f92aa882e8aab1cc2e38f8e63a2e
|
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: 1.21.6
|
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 0x7f6aea5173a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6aea517430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6aea5174c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6aea517550>", "_build": "<function ActorCriticPolicy._build at 0x7f6aea5175e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f6aea517670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6aea517700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6aea517790>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6aea517820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6aea5178b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6aea517940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6aea5179d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6aea50eed0>"}, "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": 2200000, "_total_timesteps": 2200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674742653733934877, "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:": "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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": 68750, "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": "1.21.6", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6fcaaa8e97fb0ba76dbdb63d12b000ccf4538da3b87fb338285a200ce07a3a74
|
3 |
+
size 1294506
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 2159.8258715835864, "std_reward": 43.322226145611396, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-26T15:22:14.746624"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:49e496bf26ba96b79a69fc9e94727ef9b21fb5ec71f2700d47600ae765879989
|
3 |
+
size 2136
|