Commit
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Parent(s):
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Browse files- PPO-PandaPickAndPlaceDense-v2.zip +3 -0
- PPO-PandaPickAndPlaceDense-v2/_stable_baselines3_version +1 -0
- PPO-PandaPickAndPlaceDense-v2/data +100 -0
- PPO-PandaPickAndPlaceDense-v2/policy.optimizer.pth +3 -0
- PPO-PandaPickAndPlaceDense-v2/policy.pth +3 -0
- PPO-PandaPickAndPlaceDense-v2/pytorch_variables.pth +3 -0
- PPO-PandaPickAndPlaceDense-v2/system_info.txt +7 -0
- README.md +37 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
PPO-PandaPickAndPlaceDense-v2.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:2c5f2d81e435fc5d988f09f9996980643b94a07f52a91ef5556ae98ccff9dff0
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size 3374283
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PPO-PandaPickAndPlaceDense-v2/_stable_baselines3_version
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1.8.0
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PPO-PandaPickAndPlaceDense-v2/data
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{
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"policy_class": {
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"__module__": "stable_baselines3.common.policies",
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"__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 ",
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PPO-PandaPickAndPlaceDense-v2/policy.optimizer.pth
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PPO-PandaPickAndPlaceDense-v2/policy.pth
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ADDED
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PPO-PandaPickAndPlaceDense-v2/system_info.txt
ADDED
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|
1 |
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- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
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+
- Python: 3.9.16
|
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- Stable-Baselines3: 1.8.0
|
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- PyTorch: 2.0.0+cu118
|
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- GPU Enabled: True
|
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- Numpy: 1.22.4
|
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- Gym: 0.21.0
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README.md
ADDED
@@ -0,0 +1,37 @@
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|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- PandaPickAndPlaceDense-v2
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
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model-index:
|
9 |
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- name: PPO
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: PandaPickAndPlaceDense-v2
|
16 |
+
type: PandaPickAndPlaceDense-v2
|
17 |
+
metrics:
|
18 |
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- type: mean_reward
|
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value: -8.33 +/- 4.94
|
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name: mean_reward
|
21 |
+
verified: false
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+
---
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+
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+
# **PPO** Agent playing **PandaPickAndPlaceDense-v2**
|
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+
This is a trained model of a **PPO** agent playing **PandaPickAndPlaceDense-v2**
|
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+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
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+
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+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
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+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
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+
{"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). 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