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: -3.67 +/- 0.85
|
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 sha256:650140ef1c386be14b0c11b9bd53622a2ef18bdcd468698aca4472f099cdbc96
|
3 |
+
size 108111
|
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 0x7f9adddbf310>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f9adddbe840>"
|
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:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu",
|
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": 1680689123326476158,
|
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": "[[ 0.41045132 -0.01873555 0.56906635]\n [ 0.41045132 -0.01873555 0.56906635]\n [ 0.41045132 -0.01873555 0.56906635]\n [ 0.41045132 -0.01873555 0.56906635]]",
|
60 |
+
"desired_goal": "[[ 0.18162265 0.49926198 0.75523806]\n [-1.2764933 -0.93937206 1.158484 ]\n [-0.31122163 0.10472098 -0.56227726]\n [-1.6237594 1.3485174 -1.42461 ]]",
|
61 |
+
"observation": "[[ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]\n [ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]\n [ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]\n [ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]]"
|
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": "[[ 0.05220401 -0.00032072 0.24187829]\n [ 0.0048033 0.06157379 0.12621309]\n [-0.04152822 -0.0521327 0.1565934 ]\n [-0.0161789 0.13346706 0.15228662]]",
|
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 sha256:67a38eae2541d2428d540b87a7f808e8dac6a5ecff38076b9b065989c7a3c73a
|
3 |
+
size 44734
|
a2c-PandaReachDense-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:12ddba124f425f87f72d1d9d34d7d6e4b77176a4a987a3ffa1c848ed9bd6e0e4
|
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.9.16
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 2.0.0+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
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 0x7f9adddbf310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9adddbe840>"}, "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:": "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", "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": 1680689123326476158, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.41045132 -0.01873555 0.56906635]\n [ 0.41045132 -0.01873555 0.56906635]\n [ 0.41045132 -0.01873555 0.56906635]\n [ 0.41045132 -0.01873555 0.56906635]]", "desired_goal": "[[ 0.18162265 0.49926198 0.75523806]\n [-1.2764933 -0.93937206 1.158484 ]\n [-0.31122163 0.10472098 -0.56227726]\n [-1.6237594 1.3485174 -1.42461 ]]", "observation": "[[ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]\n [ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]\n [ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]\n [ 4.1045132e-01 -1.8735554e-02 5.6906635e-01 1.5279875e-02\n -4.8421317e-04 1.3606907e-02]]"}, "_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": "[[ 0.05220401 -0.00032072 0.24187829]\n [ 0.0048033 0.06157379 0.12621309]\n [-0.04152822 -0.0521327 0.1565934 ]\n [-0.0161789 0.13346706 0.15228662]]", "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
Binary file (826 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -3.672034899611026, "std_reward": 0.8542806621773282, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T10:59:11.125650"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a7124ae15fb103784417da5e0c10ec119eacf36f0a6659f18c575963c2a0160a
|
3 |
+
size 3056
|