Initial commit
Browse files- README.md +37 -0
- a2c-PandaPickAndPlace-v3.zip +3 -0
- a2c-PandaPickAndPlace-v3/_stable_baselines3_version +1 -0
- a2c-PandaPickAndPlace-v3/data +97 -0
- a2c-PandaPickAndPlace-v3/policy.optimizer.pth +3 -0
- a2c-PandaPickAndPlace-v3/policy.pth +3 -0
- a2c-PandaPickAndPlace-v3/pytorch_variables.pth +3 -0
- a2c-PandaPickAndPlace-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 |
+
- PandaPickAndPlace-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: PandaPickAndPlace-v3
|
16 |
+
type: PandaPickAndPlace-v3
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: -50.00 +/- 0.00
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **A2C** Agent playing **PandaPickAndPlace-v3**
|
25 |
+
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-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-PandaPickAndPlace-v3.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d8d54da3606760e013f6824695a2e12c12e69c67e0b14dc6a4d362712237a99c
|
3 |
+
size 123091
|
a2c-PandaPickAndPlace-v3/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.0.0
|
a2c-PandaPickAndPlace-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 0x7c533745a170>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7c533744eb00>"
|
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": 1691575503065294230,
|
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": "[[-4.5135093 1.3766649 0.14075841]\n [ 0.97289103 0.45182437 0.14076945]\n [ 1.0608624 0.87064147 0.14076945]\n [-0.22805646 -0.03269406 0.14078334]]",
|
34 |
+
"desired_goal": "[[-0.4277546 -0.7405673 1.0112106 ]\n [-0.56035686 1.4987825 -1.0827667 ]\n [ 1.1089393 1.0786698 1.4070234 ]\n [-0.53250426 0.7551942 -1.0827667 ]]",
|
35 |
+
"observation": "[[-1.8491844e+00 1.2719667e+00 4.2453194e-01 -8.3704358e-01\n 8.9428449e-01 1.0404582e+00 -8.7112486e-01 -4.5135093e+00\n 1.3766649e+00 1.4075841e-01 -5.6209238e-03 -1.7837768e-02\n -9.3758035e-01 -1.0000000e+01 2.8795159e+00 6.3711204e-02\n -5.0517619e-02 1.5097781e-01 8.6213551e+00]\n [ 1.7029463e-01 7.6801139e-01 2.0651047e+00 -6.0979005e-02\n 8.4773026e-02 2.9118115e-01 1.3664459e+00 9.7289103e-01\n 4.5182437e-01 1.4076945e-01 -1.4137514e-02 -3.3720233e-03\n -1.6723547e-02 3.4976214e-02 3.2829575e-02 6.2620178e-02\n -5.5568912e-03 -1.6784145e-02 1.2518173e-02]\n [ 6.8823737e-01 -4.9016219e-01 -7.2988421e-01 3.7284189e-01\n 1.1661059e+00 7.7491917e-02 1.3664539e+00 1.0608624e+00\n 8.7064147e-01 1.4076945e-01 -1.4137514e-02 -3.3720231e-03\n -1.6900742e-02 3.4976229e-02 3.2829583e-02 6.2620178e-02\n -5.5568912e-03 -1.6784145e-02 1.2518121e-02]\n [ 1.5712382e-01 9.6143317e-01 1.9779571e+00 -3.0474526e-01\n 5.4665488e-01 3.6044577e-01 1.3664461e+00 -2.2805646e-01\n -3.2694060e-02 1.4078334e-01 -1.4419087e-02 -3.3484804e-03\n -1.5299192e-02 3.5342611e-02 3.3071812e-02 6.2620178e-02\n -5.5568758e-03 -1.6784130e-02 1.2919200e-02]]"
|
36 |
+
},
|
37 |
+
"_last_episode_starts": {
|
38 |
+
":type:": "<class 'numpy.ndarray'>",
|
39 |
+
":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
|
40 |
+
},
|
41 |
+
"_last_original_obs": {
|
42 |
+
":type:": "<class 'collections.OrderedDict'>",
|
43 |
+
":serialized:": "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",
|
44 |
+
"achieved_goal": "[[ 0.03898178 -0.00770499 0.02 ]\n [-0.11279402 -0.03201776 0.02 ]\n [-0.10581366 -0.09889471 0.02 ]\n [ 0.06136367 0.08723505 0.02 ]]",
|
45 |
+
"desired_goal": "[[ 0.11762659 0.00874713 0.02 ]\n [ 0.06929049 -0.14145671 0.07116944]\n [-0.09469196 -0.06014485 0.21450065]\n [-0.01397785 0.146615 0.02 ]]",
|
46 |
+
"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00 0.0000000e+00 3.8981777e-02\n -7.7049895e-03 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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 0.0000000e+00 -1.1279402e-01\n -3.2017760e-02 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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 0.0000000e+00 -1.0581366e-01\n -9.8894708e-02 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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 0.0000000e+00 6.1363667e-02\n 8.7235048e-02 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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:": "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",
|
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, (19,), 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 True]",
|
82 |
+
"bounded_above": "[ True True True True]",
|
83 |
+
"_shape": [
|
84 |
+
4
|
85 |
+
],
|
86 |
+
"low": "[-1. -1. -1. -1.]",
|
87 |
+
"high": "[1. 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-PandaPickAndPlace-v3/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:17a01e9c415984e247c467a9e64a28446bd39b9f701977fb77ba0d472ff5a902
|
3 |
+
size 51646
|
a2c-PandaPickAndPlace-v3/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f93bb8b2756981125b1cb9731ab8f2345162396d5358b29f8cc51ecdc82edcfb
|
3 |
+
size 52926
|
a2c-PandaPickAndPlace-v3/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-PandaPickAndPlace-v3/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
|
2 |
+
- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.0.0
|
4 |
+
- PyTorch: 2.0.1+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.23.5
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
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 0x7c533745a170>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c533744eb00>"}, "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": 1691575503065294230, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-4.5135093 1.3766649 0.14075841]\n [ 0.97289103 0.45182437 0.14076945]\n [ 1.0608624 0.87064147 0.14076945]\n [-0.22805646 -0.03269406 0.14078334]]", "desired_goal": "[[-0.4277546 -0.7405673 1.0112106 ]\n [-0.56035686 1.4987825 -1.0827667 ]\n [ 1.1089393 1.0786698 1.4070234 ]\n [-0.53250426 0.7551942 -1.0827667 ]]", "observation": "[[-1.8491844e+00 1.2719667e+00 4.2453194e-01 -8.3704358e-01\n 8.9428449e-01 1.0404582e+00 -8.7112486e-01 -4.5135093e+00\n 1.3766649e+00 1.4075841e-01 -5.6209238e-03 -1.7837768e-02\n -9.3758035e-01 -1.0000000e+01 2.8795159e+00 6.3711204e-02\n -5.0517619e-02 1.5097781e-01 8.6213551e+00]\n [ 1.7029463e-01 7.6801139e-01 2.0651047e+00 -6.0979005e-02\n 8.4773026e-02 2.9118115e-01 1.3664459e+00 9.7289103e-01\n 4.5182437e-01 1.4076945e-01 -1.4137514e-02 -3.3720233e-03\n -1.6723547e-02 3.4976214e-02 3.2829575e-02 6.2620178e-02\n -5.5568912e-03 -1.6784145e-02 1.2518173e-02]\n [ 6.8823737e-01 -4.9016219e-01 -7.2988421e-01 3.7284189e-01\n 1.1661059e+00 7.7491917e-02 1.3664539e+00 1.0608624e+00\n 8.7064147e-01 1.4076945e-01 -1.4137514e-02 -3.3720231e-03\n -1.6900742e-02 3.4976229e-02 3.2829583e-02 6.2620178e-02\n -5.5568912e-03 -1.6784145e-02 1.2518121e-02]\n [ 1.5712382e-01 9.6143317e-01 1.9779571e+00 -3.0474526e-01\n 5.4665488e-01 3.6044577e-01 1.3664461e+00 -2.2805646e-01\n -3.2694060e-02 1.4078334e-01 -1.4419087e-02 -3.3484804e-03\n -1.5299192e-02 3.5342611e-02 3.3071812e-02 6.2620178e-02\n -5.5568758e-03 -1.6784130e-02 1.2919200e-02]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.03898178 -0.00770499 0.02 ]\n [-0.11279402 -0.03201776 0.02 ]\n [-0.10581366 -0.09889471 0.02 ]\n [ 0.06136367 0.08723505 0.02 ]]", "desired_goal": "[[ 0.11762659 0.00874713 0.02 ]\n [ 0.06929049 -0.14145671 0.07116944]\n [-0.09469196 -0.06014485 0.21450065]\n [-0.01397785 0.146615 0.02 ]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00 0.0000000e+00 3.8981777e-02\n -7.7049895e-03 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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 0.0000000e+00 -1.1279402e-01\n -3.2017760e-02 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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 0.0000000e+00 -1.0581366e-01\n -9.8894708e-02 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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 0.0000000e+00 6.1363667e-02\n 8.7235048e-02 2.0000000e-02 0.0000000e+00 -0.0000000e+00\n 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n 0.0000000e+00 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, (19,), 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 True]", "bounded_above": "[ True True True True]", "_shape": [4], "low": "[-1. -1. -1. -1.]", "high": "[1. 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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
replay.mp4
ADDED
Binary file (784 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -50.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-09T11:10:15.705266"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6a8659e806d9b72ed3a5d807df7b26778a5df857fb146da3811771f142767be4
|
3 |
+
size 3013
|