Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization

[Paper] [Project Website]

Overview

This is the official PyTorch implementation of the paper "Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization". Our approach, CP3ER, significantly enhances the stability and performance of visual reinforcement learning models.

Installation

Setup

To install the required packages for DeepMind Control Suite and Metaworld, please run the following commands:

conda env create -f cp3er.yaml # for dmc
 
conda env create -f cp3ermw.yaml  # for metaworld

Then, install the Metaworld package:

conda activate cp3ermw
cd Metaworld
pip install -e .

Reproducing Experimental Results

Training for dmc tasks

python train.py task=acrobot_swingup

You can decide whether to use wandb to log your experiment process by specifying the 'use_wb' parameter, and determine whether to use a GPU for training by specifying the 'device' parameter. For more parameter options, please refer to the cfgs/config.yaml file.

python train.py task=cheetah_run device=cuda:1 use_wb=True seed=1

Training for metaworld tasks

Similar to training for DMC tasks, you can run the following scripts for testing in Metaworld:

python train_mw.py task=assembly-v2

Citation

If you find our research helpful and would like to reference it in your work, please consider citing the paper as follows:

@misc{li2024generalizingconsistencypolicyvisual,
      title={Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization}, 
      author={Haoran Li and Zhennan Jiang and Yuhui Chen and Dongbin Zhao},
      year={2024},
      eprint={2410.00051},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.00051}, 
}

Acknowledgement

CP3ER is licensed under the MIT license. MuJoCo and DeepMind Control Suite are licensed under the Apache 2.0 license. We would like to thank DrQ-v2 authors for open-sourcing the DrQv2 codebase. Our implementation builds on top of their repository.