Initial commit
Browse files- README.md +37 -0
- a2c-PandaReachDense-v3.zip +3 -0
- a2c-PandaReachDense-v3/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v3/data +97 -0
- a2c-PandaReachDense-v3/policy.optimizer.pth +3 -0
- a2c-PandaReachDense-v3/policy.pth +3 -0
- a2c-PandaReachDense-v3/pytorch_variables.pth +3 -0
- a2c-PandaReachDense-v3/system_info.txt +9 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- PandaReachDense-v3
|
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: PandaReachDense-v3
|
16 |
+
type: PandaReachDense-v3
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: -0.22 +/- 0.09
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **A2C** Agent playing **PandaReachDense-v3**
|
25 |
+
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
|
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-PandaReachDense-v3.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:44a6e4db562c76944991173af218768a02fd8e0f9dfb11b719945189f6168e5c
|
3 |
+
size 108215
|
a2c-PandaReachDense-v3/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.1.0
|
a2c-PandaReachDense-v3/data
ADDED
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
|
5 |
+
"__module__": "stable_baselines3.common.policies",
|
6 |
+
"__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x789e3b817400>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x789e3b80f840>"
|
10 |
+
},
|
11 |
+
"verbose": 1,
|
12 |
+
"policy_kwargs": {
|
13 |
+
":type:": "<class 'dict'>",
|
14 |
+
":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
|
15 |
+
"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
|
16 |
+
"optimizer_kwargs": {
|
17 |
+
"alpha": 0.99,
|
18 |
+
"eps": 1e-05,
|
19 |
+
"weight_decay": 0
|
20 |
+
}
|
21 |
+
},
|
22 |
+
"num_timesteps": 1000000,
|
23 |
+
"_total_timesteps": 1000000,
|
24 |
+
"_num_timesteps_at_start": 0,
|
25 |
+
"seed": null,
|
26 |
+
"action_noise": null,
|
27 |
+
"start_time": 1699205497171180075,
|
28 |
+
"learning_rate": 0.0007,
|
29 |
+
"tensorboard_log": null,
|
30 |
+
"_last_obs": {
|
31 |
+
":type:": "<class 'collections.OrderedDict'>",
|
32 |
+
":serialized:": "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",
|
33 |
+
"achieved_goal": "[[-0.10713648 0.43382344 -0.19112523]\n [-1.4464462 0.42103443 0.5847789 ]\n [ 0.24525055 0.0064759 0.41063145]\n [ 0.24525055 0.0064759 0.41063145]]",
|
34 |
+
"desired_goal": "[[ 0.02817576 1.1321938 -0.0095447 ]\n [-1.4088728 0.24986376 0.38055697]\n [ 0.39562073 -1.033929 -0.21570237]\n [-0.18876639 -1.0479422 0.48136345]]",
|
35 |
+
"observation": "[[-1.0713648e-01 4.3382344e-01 -1.9112523e-01 -1.8192952e+00\n 1.6622366e+00 -1.3938873e+00]\n [-1.4464462e+00 4.2103443e-01 5.8477890e-01 -7.8011090e-01\n 5.5085972e-02 1.4802846e+00]\n [ 2.4525055e-01 6.4758970e-03 4.1063145e-01 4.5394415e-01\n 3.9545709e-04 3.7343660e-01]\n [ 2.4525055e-01 6.4758970e-03 4.1063145e-01 4.5394415e-01\n 3.9545709e-04 3.7343660e-01]]"
|
36 |
+
},
|
37 |
+
"_last_episode_starts": {
|
38 |
+
":type:": "<class 'numpy.ndarray'>",
|
39 |
+
":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
|
40 |
+
},
|
41 |
+
"_last_original_obs": {
|
42 |
+
":type:": "<class 'collections.OrderedDict'>",
|
43 |
+
":serialized:": "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",
|
44 |
+
"achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
|
45 |
+
"desired_goal": "[[-0.0437986 -0.13473198 0.12070036]\n [ 0.12345515 0.0800622 0.26795277]\n [-0.06060426 -0.03172041 0.26863328]\n [ 0.07214285 0.1362134 0.10034851]]",
|
46 |
+
"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
|
47 |
+
},
|
48 |
+
"_episode_num": 0,
|
49 |
+
"use_sde": false,
|
50 |
+
"sde_sample_freq": -1,
|
51 |
+
"_current_progress_remaining": 0.0,
|
52 |
+
"_stats_window_size": 100,
|
53 |
+
"ep_info_buffer": {
|
54 |
+
":type:": "<class 'collections.deque'>",
|
55 |
+
":serialized:": "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"
|
56 |
+
},
|
57 |
+
"ep_success_buffer": {
|
58 |
+
":type:": "<class 'collections.deque'>",
|
59 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
60 |
+
},
|
61 |
+
"_n_updates": 50000,
|
62 |
+
"n_steps": 5,
|
63 |
+
"gamma": 0.99,
|
64 |
+
"gae_lambda": 1.0,
|
65 |
+
"ent_coef": 0.0,
|
66 |
+
"vf_coef": 0.5,
|
67 |
+
"max_grad_norm": 0.5,
|
68 |
+
"normalize_advantage": false,
|
69 |
+
"observation_space": {
|
70 |
+
":type:": "<class 'gymnasium.spaces.dict.Dict'>",
|
71 |
+
":serialized:": "gAWVsAMAAAAAAACMFWd5bW5hc2l1bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwUZ3ltbmFzaXVtLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowNYm91bmRlZF9iZWxvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoHCiWAwAAAAAAAAABAQGUaCBLA4WUaCR0lFKUjAZfc2hhcGWUSwOFlIwDbG93lGgcKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoJHSUUpSMBGhpZ2iUaBwolgwAAAAAAAAAAAAgQQAAIEEAACBBlGgWSwOFlGgkdJRSlIwIbG93X3JlcHKUjAUtMTAuMJSMCWhpZ2hfcmVwcpSMBDEwLjCUjApfbnBfcmFuZG9tlE51YowMZGVzaXJlZF9nb2FslGgNKYGUfZQoaBBoFmgZaBwolgMAAAAAAAAAAQEBlGggSwOFlGgkdJRSlGgnaBwolgMAAAAAAAAAAQEBlGggSwOFlGgkdJRSlGgsSwOFlGguaBwolgwAAAAAAAAAAAAgwQAAIMEAACDBlGgWSwOFlGgkdJRSlGgzaBwolgwAAAAAAAAAAAAgQQAAIEEAACBBlGgWSwOFlGgkdJRSlGg4jAUtMTAuMJRoOowEMTAuMJRoPE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBloHCiWBgAAAAAAAAABAQEBAQGUaCBLBoWUaCR0lFKUaCdoHCiWBgAAAAAAAAABAQEBAQGUaCBLBoWUaCR0lFKUaCxLBoWUaC5oHCiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCR0lFKUaDNoHCiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCR0lFKUaDiMBS0xMC4wlGg6jAQxMC4wlGg8TnVidWgsTmgQTmg8TnViLg==",
|
72 |
+
"spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
|
73 |
+
"_shape": null,
|
74 |
+
"dtype": null,
|
75 |
+
"_np_random": null
|
76 |
+
},
|
77 |
+
"action_space": {
|
78 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
79 |
+
":serialized:": "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",
|
80 |
+
"dtype": "float32",
|
81 |
+
"bounded_below": "[ True True True]",
|
82 |
+
"bounded_above": "[ True True True]",
|
83 |
+
"_shape": [
|
84 |
+
3
|
85 |
+
],
|
86 |
+
"low": "[-1. -1. -1.]",
|
87 |
+
"high": "[1. 1. 1.]",
|
88 |
+
"low_repr": "-1.0",
|
89 |
+
"high_repr": "1.0",
|
90 |
+
"_np_random": null
|
91 |
+
},
|
92 |
+
"n_envs": 4,
|
93 |
+
"lr_schedule": {
|
94 |
+
":type:": "<class 'function'>",
|
95 |
+
":serialized:": "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"
|
96 |
+
}
|
97 |
+
}
|
a2c-PandaReachDense-v3/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:059d7c8dde6d627f22396660f14732bb9f992cbf220250083e355557c62cd014
|
3 |
+
size 45167
|
a2c-PandaReachDense-v3/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1f988eb6061f2528113d93ec955516d6d274ccd040387e06e1958157701bf95c
|
3 |
+
size 46447
|
a2c-PandaReachDense-v3/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
|
3 |
+
size 864
|
a2c-PandaReachDense-v3/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
|
2 |
+
- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.1.0
|
4 |
+
- PyTorch: 2.1.0+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.23.5
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.29.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x789e3b817400>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x789e3b80f840>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699205497171180075, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.10713648 0.43382344 -0.19112523]\n [-1.4464462 0.42103443 0.5847789 ]\n [ 0.24525055 0.0064759 0.41063145]\n [ 0.24525055 0.0064759 0.41063145]]", "desired_goal": "[[ 0.02817576 1.1321938 -0.0095447 ]\n [-1.4088728 0.24986376 0.38055697]\n [ 0.39562073 -1.033929 -0.21570237]\n [-0.18876639 -1.0479422 0.48136345]]", "observation": "[[-1.0713648e-01 4.3382344e-01 -1.9112523e-01 -1.8192952e+00\n 1.6622366e+00 -1.3938873e+00]\n [-1.4464462e+00 4.2103443e-01 5.8477890e-01 -7.8011090e-01\n 5.5085972e-02 1.4802846e+00]\n [ 2.4525055e-01 6.4758970e-03 4.1063145e-01 4.5394415e-01\n 3.9545709e-04 3.7343660e-01]\n [ 2.4525055e-01 6.4758970e-03 4.1063145e-01 4.5394415e-01\n 3.9545709e-04 3.7343660e-01]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.0437986 -0.13473198 0.12070036]\n [ 0.12345515 0.0800622 0.26795277]\n [-0.06060426 -0.03172041 0.26863328]\n [ 0.07214285 0.1362134 0.10034851]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "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": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}
|
replay.mp4
ADDED
Binary file (655 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -0.2170967184007168, "std_reward": 0.08655009569243335, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-05T18:22:25.265472"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:991f80068d6fb877beb44a155e4f69cd198c5cebf7f3cf77d8a0ed1881ad071f
|
3 |
+
size 2636
|