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: -5.64 +/- 1.25
|
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:f3abb2f494829eb366790de20d9a8f5118c539a174dc757b8289a27a6843abf4
|
3 |
+
size 108028
|
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 0x7fbf9499eaf0>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fbf949a1800>"
|
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": 1680114452548897626,
|
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": "[[ 0.4668016 -0.02726415 0.5859499 ]\n [ 0.4668016 -0.02726415 0.5859499 ]\n [ 0.4668016 -0.02726415 0.5859499 ]\n [ 0.4668016 -0.02726415 0.5859499 ]]",
|
60 |
+
"desired_goal": "[[-0.06474052 -0.22892119 -1.1876967 ]\n [ 0.45559382 0.03316247 0.117513 ]\n [ 0.95784014 -0.7278974 0.8523865 ]\n [-0.05988758 -0.4404228 -1.0930167 ]]",
|
61 |
+
"observation": "[[ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]\n [ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]\n [ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]\n [ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]]"
|
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": "[[ 0.10293267 0.08690699 0.01455194]\n [ 0.10063607 -0.14166132 0.0273832 ]\n [ 0.14957182 -0.02600973 0.13913335]\n [-0.0404806 -0.05538164 0.2322577 ]]",
|
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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI9P3UeOnGFMCUhpRSlIwBbJRLMowBdJRHQKZQNVfeDWd1fZQoaAZoCWgPQwhBn8iTpEsLwJSGlFKUaBVLMmgWR0CmT/fukUKzdX2UKGgGaAloD0MIrye6LvzwFMCUhpRSlGgVSzJoFkdApk+4Mc6vJXV9lChoBmgJaA9DCNYcIJijpw3AlIaUUpRoFUsyaBZHQKZPejxkNF11fZQoaAZoCWgPQwjP2JdsPMgQwJSGlFKUaBVLMmgWR0CmUZbyYoiLdX2UKGgGaAloD0MIM+AsJcs5E8CUhpRSlGgVSzJoFkdAplFZuO0b+HV9lChoBmgJaA9DCBlxAWiU/hXAlIaUUpRoFUsyaBZHQKZRGlxffGd1fZQoaAZoCWgPQwijW6/pQYESwJSGlFKUaBVLMmgWR0CmUNzKcNH6dX2UKGgGaAloD0MIOXtntFUZE8CUhpRSlGgVSzJoFkdAplLnv8ZUDXV9lChoBmgJaA9DCASuK2aE9xjAlIaUUpRoFUsyaBZHQKZSqm5UcXF1fZQoaAZoCWgPQwhPIOwUqyYOwJSGlFKUaBVLMmgWR0CmUmsQmNR4dX2UKGgGaAloD0MIjUXT2ckgFsCUhpRSlGgVSzJoFkdAplIto11nunV9lChoBmgJaA9DCIe/JmvUwwrAlIaUUpRoFUsyaBZHQKZUShIvrW11fZQoaAZoCWgPQwiRe7q6YxEcwJSGlFKUaBVLMmgWR0CmVAzPrv9cdX2UKGgGaAloD0MIc0urIXEvE8CUhpRSlGgVSzJoFkdAplPOJJoTPHV9lChoBmgJaA9DCMBcixagLRHAlIaUUpRoFUsyaBZHQKZTkJvYODt1fZQoaAZoCWgPQwiLql/pfLgXwJSGlFKUaBVLMmgWR0CmVbT8YQ8PdX2UKGgGaAloD0MItHVwsDfhFcCUhpRSlGgVSzJoFkdAplV3yAhB7nV9lChoBmgJaA9DCD9XW7G/XCDAlIaUUpRoFUsyaBZHQKZVOIcBEKF1fZQoaAZoCWgPQwiEgHwJFXQhwJSGlFKUaBVLMmgWR0CmVPsF2V3VdX2UKGgGaAloD0MIJNbiUwC8FcCUhpRSlGgVSzJoFkdAplc+Xb/OuHV9lChoBmgJaA9DCE64V+atOgbAlIaUUpRoFUsyaBZHQKZXAeBg/kh1fZQoaAZoCWgPQwiQiCmRRA8RwJSGlFKUaBVLMmgWR0CmVsNE5QxfdX2UKGgGaAloD0MI76oHzEN2HMCUhpRSlGgVSzJoFkdAplaF9+gDinV9lChoBmgJaA9DCGoX00z3mhHAlIaUUpRoFUsyaBZHQKZYs7tAs051fZQoaAZoCWgPQwhAv+/fvBgWwJSGlFKUaBVLMmgWR0CmWHaHbh3rdX2UKGgGaAloD0MIdJfEWRHVEMCUhpRSlGgVSzJoFkdAplg3P5YYBXV9lChoBmgJaA9DCBwJNNjU4SDAlIaUUpRoFUsyaBZHQKZX+dXDFZR1fZQoaAZoCWgPQwgbZf1mYjoOwJSGlFKUaBVLMmgWR0CmWiDPfKp2dX2UKGgGaAloD0MIb5upEI9kDMCUhpRSlGgVSzJoFkdAplnkEovzv3V9lChoBmgJaA9DCONxUS0iig3AlIaUUpRoFUsyaBZHQKZZpM3ZPEd1fZQoaAZoCWgPQwiAKm7cYo4VwJSGlFKUaBVLMmgWR0CmWWdfb9IgdX2UKGgGaAloD0MIBJFFmnhnDcCUhpRSlGgVSzJoFkdAplsJ8a4tpXV9lChoBmgJaA9DCK36XG3Fvg3AlIaUUpRoFUsyaBZHQKZazLkjopx1fZQoaAZoCWgPQwj59q5BX5oUwJSGlFKUaBVLMmgWR0CmWozxXnyNdX2UKGgGaAloD0MIr9FyoIc6DMCUhpRSlGgVSzJoFkdAplpO7lJYknV9lChoBmgJaA9DCBr9aDhlbg/AlIaUUpRoFUsyaBZHQKZb7JHy3Ct1fZQoaAZoCWgPQwjwTdNnB/wNwJSGlFKUaBVLMmgWR0CmW67YbsF/dX2UKGgGaAloD0MI/nxbsFRHEMCUhpRSlGgVSzJoFkdApltu+ueSS3V9lChoBmgJaA9DCHHGMCdoUxvAlIaUUpRoFUsyaBZHQKZbMO4oZyd1fZQoaAZoCWgPQwjl8bT8wOUTwJSGlFKUaBVLMmgWR0CmXMrMLWqcdX2UKGgGaAloD0MI761ITFAjF8CUhpRSlGgVSzJoFkdAplyNI3BHkXV9lChoBmgJaA9DCNYaSu1F9A3AlIaUUpRoFUsyaBZHQKZcTU3GXHB1fZQoaAZoCWgPQwhH6GfqdXsZwJSGlFKUaBVLMmgWR0CmXA9ZRsMzdX2UKGgGaAloD0MIVfoJZ7e2EMCUhpRSlGgVSzJoFkdApl2qBAfMfXV9lChoBmgJaA9DCO+SOCuiphbAlIaUUpRoFUsyaBZHQKZdbFXJYDF1fZQoaAZoCWgPQwhLdJZZhDIfwJSGlFKUaBVLMmgWR0CmXSxqO939dX2UKGgGaAloD0MIT8+7saBwFMCUhpRSlGgVSzJoFkdAplzueg+Ql3V9lChoBmgJaA9DCBNhw9MrZRbAlIaUUpRoFUsyaBZHQKZekDtgKF91fZQoaAZoCWgPQwiRD3o2q34LwJSGlFKUaBVLMmgWR0CmXlJu/DcedX2UKGgGaAloD0MIaYzWUdXUGMCUhpRSlGgVSzJoFkdApl4SvicXnHV9lChoBmgJaA9DCLKBdLFp5QPAlIaUUpRoFUsyaBZHQKZd1O1OTJR1fZQoaAZoCWgPQwhOKa+V0F0GwJSGlFKUaBVLMmgWR0CmX3sXJo0zdX2UKGgGaAloD0MIfNEeL6QzEcCUhpRSlGgVSzJoFkdApl89TP0I1XV9lChoBmgJaA9DCIodjUP97g7AlIaUUpRoFUsyaBZHQKZe/llsguB1fZQoaAZoCWgPQwhjmX6JeHsUwJSGlFKUaBVLMmgWR0CmXsBw++uedX2UKGgGaAloD0MIbEPFOH8TGsCUhpRSlGgVSzJoFkdApmBX69CeE3V9lChoBmgJaA9DCPusMlNarxXAlIaUUpRoFUsyaBZHQKZgGhHskY51fZQoaAZoCWgPQwiVEKyql08TwJSGlFKUaBVLMmgWR0CmX9oqkM1CdX2UKGgGaAloD0MI0Xr4MlEkD8CUhpRSlGgVSzJoFkdApl+cL+glGHV9lChoBmgJaA9DCLXEymjk8wbAlIaUUpRoFUsyaBZHQKZhNlmvnr91fZQoaAZoCWgPQwjEXFK13YQOwJSGlFKUaBVLMmgWR0CmYPjUutfYdX2UKGgGaAloD0MIMpI9Qs3AE8CUhpRSlGgVSzJoFkdApmC5XCCSR3V9lChoBmgJaA9DCKd2hqktRR3AlIaUUpRoFUsyaBZHQKZge4vvjOt1fZQoaAZoCWgPQwjfFcH/ViIXwJSGlFKUaBVLMmgWR0CmYh7o0Q9SdX2UKGgGaAloD0MI3q8CfLfpEcCUhpRSlGgVSzJoFkdApmHhDu0CzXV9lChoBmgJaA9DCHmRCfg1whPAlIaUUpRoFUsyaBZHQKZhoTAWSEF1fZQoaAZoCWgPQwjkg57Nqh8VwJSGlFKUaBVLMmgWR0CmYWMibDuSdX2UKGgGaAloD0MIcEIhAg4RFcCUhpRSlGgVSzJoFkdApmL2xfOUuHV9lChoBmgJaA9DCOPHmLuWcA3AlIaUUpRoFUsyaBZHQKZiuPyTY/V1fZQoaAZoCWgPQwjTSiGQSwwcwJSGlFKUaBVLMmgWR0CmYnkbxVhkdX2UKGgGaAloD0MI9gg1Q6p4EsCUhpRSlGgVSzJoFkdApmI7EUCaJHV9lChoBmgJaA9DCErSNZNvBiHAlIaUUpRoFUsyaBZHQKZj5Kh+OOt1fZQoaAZoCWgPQwhzZOWXwQgXwJSGlFKUaBVLMmgWR0CmY6ceS0SidX2UKGgGaAloD0MIwoU8ghvpGMCUhpRSlGgVSzJoFkdApmNnR9gF5nV9lChoBmgJaA9DCMkdNpGZawbAlIaUUpRoFUsyaBZHQKZjKXqJMxp1fZQoaAZoCWgPQwjx1CMNbtshwJSGlFKUaBVLMmgWR0CmZMHkT6BRdX2UKGgGaAloD0MIs0C7Q4phFMCUhpRSlGgVSzJoFkdApmSEfaHsTnV9lChoBmgJaA9DCESi0LLu3w/AlIaUUpRoFUsyaBZHQKZkRKFIuoR1fZQoaAZoCWgPQwh+qgoNxLIOwJSGlFKUaBVLMmgWR0CmZAaIN3GGdX2UKGgGaAloD0MIJ/kRv2INE8CUhpRSlGgVSzJoFkdApmWh3mmtQ3V9lChoBmgJaA9DCC+lLhnHCAbAlIaUUpRoFUsyaBZHQKZlZDcdo391fZQoaAZoCWgPQwjHEAAce5YLwJSGlFKUaBVLMmgWR0CmZSSsbNr1dX2UKGgGaAloD0MIF7ZmKy8RIcCUhpRSlGgVSzJoFkdApmTmpuMuOHV9lChoBmgJaA9DCBowSPq0mhPAlIaUUpRoFUsyaBZHQKZmkaCtihF1fZQoaAZoCWgPQwgyO4veqeAHwJSGlFKUaBVLMmgWR0CmZlPMKTjedX2UKGgGaAloD0MIIlUUr7K2EcCUhpRSlGgVSzJoFkdApmYUC3gDR3V9lChoBmgJaA9DCHE8nwH1Jg3AlIaUUpRoFUsyaBZHQKZl1huwX691fZQoaAZoCWgPQwiHi9zT1d0KwJSGlFKUaBVLMmgWR0CmZ21DKHO9dX2UKGgGaAloD0MIF0hQ/BizC8CUhpRSlGgVSzJoFkdApmcvcvduYXV9lChoBmgJaA9DCOoI4GbxQgXAlIaUUpRoFUsyaBZHQKZm76PbO/t1fZQoaAZoCWgPQwjdQ8L3/sYUwJSGlFKUaBVLMmgWR0CmZrGecx0udX2UKGgGaAloD0MImnyzzY2RIMCUhpRSlGgVSzJoFkdApmhJTwUg0XV9lChoBmgJaA9DCC7L12X4jw3AlIaUUpRoFUsyaBZHQKZoC4UeuFJ1fZQoaAZoCWgPQwjvObAcIQMZwJSGlFKUaBVLMmgWR0CmZ8uyVv/BdX2UKGgGaAloD0MIaoe/JmsUDsCUhpRSlGgVSzJoFkdApmeNyR0U5HV9lChoBmgJaA9DCINuL2mMthfAlIaUUpRoFUsyaBZHQKZpIdRR/Ex1fZQoaAZoCWgPQwjrG5jcKDIbwJSGlFKUaBVLMmgWR0CmaOQSSNfgdX2UKGgGaAloD0MIqDej5qtUEsCUhpRSlGgVSzJoFkdApmikNc4YJnV9lChoBmgJaA9DCJ86Vik9kw7AlIaUUpRoFUsyaBZHQKZoZiw0O3F1ZS4="
|
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:cc07744d10e98379cf2c44c062367ea9143d3c82e91f06bc89204e0f521e0b06
|
3 |
+
size 44734
|
a2c-PandaReachDense-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:793d14e103f532b71881b5c5be844aa49d9d807dbe3cc07cae6b842049662d4d
|
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: 1.13.1+cu116
|
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 0x7fbf9499eaf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbf949a1800>"}, "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": 1680114452548897626, "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": "[[ 0.4668016 -0.02726415 0.5859499 ]\n [ 0.4668016 -0.02726415 0.5859499 ]\n [ 0.4668016 -0.02726415 0.5859499 ]\n [ 0.4668016 -0.02726415 0.5859499 ]]", "desired_goal": "[[-0.06474052 -0.22892119 -1.1876967 ]\n [ 0.45559382 0.03316247 0.117513 ]\n [ 0.95784014 -0.7278974 0.8523865 ]\n [-0.05988758 -0.4404228 -1.0930167 ]]", "observation": "[[ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]\n [ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]\n [ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]\n [ 0.4668016 -0.02726415 0.5859499 -0.00324136 0.00180831 0.00148996]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAXc7SPUv8sT1Ha248SBrOPaoPEb67UuA8WykZPlkS1bz5eA4+/c4lvdvXYr321G0+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "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": "[[ 0.10293267 0.08690699 0.01455194]\n [ 0.10063607 -0.14166132 0.0273832 ]\n [ 0.14957182 -0.02600973 0.13913335]\n [-0.0404806 -0.05538164 0.2322577 ]]", "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": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
Binary file (899 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -5.6353996130637825, "std_reward": 1.2534787441915693, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-29T19:18:44.035153"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f1782dc1f3321e3becb00b9d054b8c25e8db9d272a21dcd921f7beec2b85c798
|
3 |
+
size 3056
|