KubiakJakub01
commited on
Commit
•
dd5e69c
1
Parent(s):
5491f3c
Initial commit
Browse files- README.md +37 -0
- a2c-PandaReachDense-v2.zip +3 -0
- a2c-PandaReachDense-v2/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v2/data +94 -0
- a2c-PandaReachDense-v2/policy.optimizer.pth +3 -0
- a2c-PandaReachDense-v2/policy.pth +3 -0
- a2c-PandaReachDense-v2/pytorch_variables.pth +3 -0
- a2c-PandaReachDense-v2/system_info.txt +7 -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-v2
|
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-v2
|
16 |
+
type: PandaReachDense-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: -4.71 +/- 1.22
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **A2C** Agent playing **PandaReachDense-v2**
|
25 |
+
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
|
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-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f07f61bf358498dba8c16a5eb854caf15f51121c6228294ecfaa99881e631808
|
3 |
+
size 108179
|
a2c-PandaReachDense-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.7.0
|
a2c-PandaReachDense-v2/data
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7fc761f10820>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fc761f0ef40>"
|
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 |
+
"observation_space": {
|
23 |
+
":type:": "<class 'gym.spaces.dict.Dict'>",
|
24 |
+
":serialized:": "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",
|
25 |
+
"spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
|
26 |
+
"_shape": null,
|
27 |
+
"dtype": null,
|
28 |
+
"_np_random": null
|
29 |
+
},
|
30 |
+
"action_space": {
|
31 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
32 |
+
":serialized:": "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",
|
33 |
+
"dtype": "float32",
|
34 |
+
"_shape": [
|
35 |
+
3
|
36 |
+
],
|
37 |
+
"low": "[-1. -1. -1.]",
|
38 |
+
"high": "[1. 1. 1.]",
|
39 |
+
"bounded_below": "[ True True True]",
|
40 |
+
"bounded_above": "[ True True True]",
|
41 |
+
"_np_random": null
|
42 |
+
},
|
43 |
+
"n_envs": 4,
|
44 |
+
"num_timesteps": 1000000,
|
45 |
+
"_total_timesteps": 1000000,
|
46 |
+
"_num_timesteps_at_start": 0,
|
47 |
+
"seed": null,
|
48 |
+
"action_noise": null,
|
49 |
+
"start_time": 1677353986590013500,
|
50 |
+
"learning_rate": 0.0007,
|
51 |
+
"tensorboard_log": null,
|
52 |
+
"lr_schedule": {
|
53 |
+
":type:": "<class 'function'>",
|
54 |
+
":serialized:": "gAWV7wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMXi9yb290L21pbmljb25kYTMvZW52cy9kZWVwLXJsLWhmL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjF4vcm9vdC9taW5pY29uZGEzL2VudnMvZGVlcC1ybC1oZi9saWIvcHl0aG9uMy45L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
|
55 |
+
},
|
56 |
+
"_last_obs": {
|
57 |
+
":type:": "<class 'collections.OrderedDict'>",
|
58 |
+
":serialized:": "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",
|
59 |
+
"achieved_goal": "[[0.40642768 0.03529762 0.59966207]\n [0.40642768 0.03529762 0.59966207]\n [0.40642768 0.03529762 0.59966207]\n [0.40642768 0.03529762 0.59966207]]",
|
60 |
+
"desired_goal": "[[ 0.64292455 0.76327753 1.2849066 ]\n [-0.7793084 0.97719884 -0.8147252 ]\n [-1.4507585 -0.7770146 0.7302987 ]\n [ 0.05265842 -1.4540328 0.9049264 ]]",
|
61 |
+
"observation": "[[ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]\n [ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]\n [ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]\n [ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]]"
|
62 |
+
},
|
63 |
+
"_last_episode_starts": {
|
64 |
+
":type:": "<class 'numpy.ndarray'>",
|
65 |
+
":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
|
66 |
+
},
|
67 |
+
"_last_original_obs": {
|
68 |
+
":type:": "<class 'collections.OrderedDict'>",
|
69 |
+
":serialized:": "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",
|
70 |
+
"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]]",
|
71 |
+
"desired_goal": "[[-0.09335712 0.00249526 0.0767425 ]\n [ 0.09883242 0.10901816 0.21542926]\n [ 0.0100013 0.10154868 0.1181565 ]\n [-0.07437091 0.12711562 0.12567514]]",
|
72 |
+
"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]]"
|
73 |
+
},
|
74 |
+
"_episode_num": 0,
|
75 |
+
"use_sde": false,
|
76 |
+
"sde_sample_freq": -1,
|
77 |
+
"_current_progress_remaining": 0.0,
|
78 |
+
"ep_info_buffer": {
|
79 |
+
":type:": "<class 'collections.deque'>",
|
80 |
+
":serialized:": "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"
|
81 |
+
},
|
82 |
+
"ep_success_buffer": {
|
83 |
+
":type:": "<class 'collections.deque'>",
|
84 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
85 |
+
},
|
86 |
+
"_n_updates": 50000,
|
87 |
+
"n_steps": 5,
|
88 |
+
"gamma": 0.99,
|
89 |
+
"gae_lambda": 1.0,
|
90 |
+
"ent_coef": 0.0,
|
91 |
+
"vf_coef": 0.5,
|
92 |
+
"max_grad_norm": 0.5,
|
93 |
+
"normalize_advantage": false
|
94 |
+
}
|
a2c-PandaReachDense-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5952051e9af5ffbbf2a945da425882d8f9ceaf568455bcf7d2b6c4cc3b1b8602
|
3 |
+
size 44734
|
a2c-PandaReachDense-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:30a61ad4fcdc6a5609c0c9e7db6e2c32054510bd60478b537b8bd5540528409b
|
3 |
+
size 46014
|
a2c-PandaReachDense-v2/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-PandaReachDense-v2/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Fri Apr 2 22:23:49 UTC 2021
|
2 |
+
- Python: 3.9.16
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1+cu117
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.24.2
|
7 |
+
- Gym: 0.21.0
|
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 0x7fc761f10820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc761f0ef40>"}, "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}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677353986590013500, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAShfQPjyUED10gxk/ShfQPjyUED10gxk/ShfQPjyUED10gxk/ShfQPjyUED10gxk/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAtJYkPyhmQz/Sd6Q/wYBHv7Qpej/VkVC/dLK5v27qRr/b9Do/XLBXPb8dur9CqWc/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABKF9A+PJQQPXSDGT/fe4a5kWVrOX37f7tKF9A+PJQQPXSDGT/fe4a5kWVrOX37f7tKF9A+PJQQPXSDGT/fe4a5kWVrOX37f7tKF9A+PJQQPXSDGT/fe4a5kWVrOX37f7uUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[0.40642768 0.03529762 0.59966207]\n [0.40642768 0.03529762 0.59966207]\n [0.40642768 0.03529762 0.59966207]\n [0.40642768 0.03529762 0.59966207]]", "desired_goal": "[[ 0.64292455 0.76327753 1.2849066 ]\n [-0.7793084 0.97719884 -0.8147252 ]\n [-1.4507585 -0.7770146 0.7302987 ]\n [ 0.05265842 -1.4540328 0.9049264 ]]", "observation": "[[ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]\n [ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]\n [ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]\n [ 4.0642768e-01 3.5297617e-02 5.9966207e-01 -2.5650763e-04\n 2.2449183e-04 -3.9059811e-03]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.09335712 0.00249526 0.0767425 ]\n [ 0.09883242 0.10901816 0.21542926]\n [ 0.0100013 0.10154868 0.1181565 ]\n [-0.07437091 0.12711562 0.12567514]]", "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, "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, "system_info": {"OS": "Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Fri Apr 2 22:23:49 UTC 2021", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
Binary file (863 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -4.707451557833702, "std_reward": 1.2177933427846779, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-25T21:32:08.754205"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6d2bb3572d4a50450df6f1852c29187a86f6448d9b538ebfbcbe76ae87a4498e
|
3 |
+
size 3056
|