RajMoodley
commited on
Commit
•
3f83e0a
1
Parent(s):
d9861ca
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: -1.19 +/- 0.19
|
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 sha256:8110df590007947cc5af738aa00abf7460f89b7059553f034db6b3cd02dd0f51
|
3 |
+
size 108025
|
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 0x7f15a8394ca0>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc_data object at 0x7f15a8397120>"
|
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": 159644,
|
45 |
+
"_total_timesteps": 1000000,
|
46 |
+
"_num_timesteps_at_start": 0,
|
47 |
+
"seed": null,
|
48 |
+
"action_noise": null,
|
49 |
+
"start_time": 1674993793599013841,
|
50 |
+
"learning_rate": 0.0007,
|
51 |
+
"tensorboard_log": null,
|
52 |
+
"lr_schedule": {
|
53 |
+
":type:": "<class 'function'>",
|
54 |
+
":serialized:": "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"
|
55 |
+
},
|
56 |
+
"_last_obs": {
|
57 |
+
":type:": "<class 'collections.OrderedDict'>",
|
58 |
+
":serialized:": "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",
|
59 |
+
"achieved_goal": "[[-1.4889661 -1.4162743 -0.5801305 ]\n [-0.55048925 -1.4650543 -0.9654067 ]\n [-0.1581515 -1.3890975 -0.7101366 ]\n [-1.3991274 -0.22421026 1.3159263 ]]",
|
60 |
+
"desired_goal": "[[-1.6542736 -1.610492 -0.5844263 ]\n [-0.78267735 -1.4310199 -1.223353 ]\n [-0.090257 -1.6726532 -0.95412874]\n [-1.3778523 -0.12881334 1.5602419 ]]",
|
61 |
+
"observation": "[[-1.4889661 -1.4162743 -0.5801305 0.2895534 1.670037 -0.3016072 ]\n [-0.55048925 -1.4650543 -0.9654067 -0.65337265 0.8928951 -1.0008525 ]\n [-0.1581515 -1.3890975 -0.7101366 -0.69329166 -0.8477605 -0.16805203]\n [-1.3991274 -0.22421026 1.3159263 0.7432698 -0.8973249 -0.08385138]]"
|
62 |
+
},
|
63 |
+
"_last_episode_starts": {
|
64 |
+
":type:": "<class 'numpy.ndarray'>",
|
65 |
+
":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.018358 -0.10467263 0.12541305]\n [-0.00739396 0.03868124 0.11488967]\n [ 0.07565828 0.0476165 0.12629995]\n [ 0.10520785 0.08659843 0.25106728]]",
|
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": 0.84036,
|
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": 7982,
|
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 sha256:86b5b9282e143186656038ec6650cd4d9fb67a488714ddcb592e65909c7fc5e5
|
3 |
+
size 44734
|
a2c-PandaReachDense-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:37c4412ad4b8f52e37231fa0f8066f6322f7fb2177ec0219f25df380a4e3d93f
|
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-5.10.147+-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:": "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 0x7f15a8394ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f15a8397120>"}, "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": 159644, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674993793599013841, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAcZa+v3pItb9vgxS/3ewMv+aGu7/lJHe/ePIhvvLNsb+DyzW/mxazv2CXZb5GcKg/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAPb/Tv5okzr/2nBW/i11Iv6krt7/Vlpy/qti4vYAZ1r/IQXS/d12wv6XnA74Ctsc/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABxlr6/eki1v2+DFL9YQJQ+xsPVP0Jsmr7d7Ay/5oa7v+Ukd79uQye/xpRkP+8bgL948iG+8s2xv4PLNb+QezG/1QZZv9UVLL6bFrO/YJdlvkZwqD/uRj4/Frdlv0a6q72UaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[-1.4889661 -1.4162743 -0.5801305 ]\n [-0.55048925 -1.4650543 -0.9654067 ]\n [-0.1581515 -1.3890975 -0.7101366 ]\n [-1.3991274 -0.22421026 1.3159263 ]]", "desired_goal": "[[-1.6542736 -1.610492 -0.5844263 ]\n [-0.78267735 -1.4310199 -1.223353 ]\n [-0.090257 -1.6726532 -0.95412874]\n [-1.3778523 -0.12881334 1.5602419 ]]", "observation": "[[-1.4889661 -1.4162743 -0.5801305 0.2895534 1.670037 -0.3016072 ]\n [-0.55048925 -1.4650543 -0.9654067 -0.65337265 0.8928951 -1.0008525 ]\n [-0.1581515 -1.3890975 -0.7101366 -0.69329166 -0.8477605 -0.16805203]\n [-1.3991274 -0.22421026 1.3159263 0.7432698 -0.8973249 -0.08385138]]"}, "_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": "[[ 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": "[[-0.018358 -0.10467263 0.12541305]\n [-0.00739396 0.03868124 0.11488967]\n [ 0.07565828 0.0476165 0.12629995]\n [ 0.10520785 0.08659843 0.25106728]]", "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": 0.84036, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 7982, "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-5.10.147+-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
Binary file (772 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -1.1863406594609842, "std_reward": 0.1943628691117453, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-29T12:11:38.110364"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6400d1c628ec4c31726b0766e11f7b443c8cbdceb998052b9e5e26508ce419ef
|
3 |
+
size 3056
|