Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
Abstract
With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability. As a time-efficient diffusion model, although consistency models have been validated in online state-based RL, it is still an open question whether it can be extended to visual RL. In this paper, we investigate the impact of non-stationary distribution and the actor-critic framework on consistency policy in online RL, and find that consistency policy was unstable during the training, especially in visual RL with the high-dimensional state space. To this end, we suggest sample-based entropy regularization to stabilize the policy training, and propose a consistency policy with prioritized proximal experience regularization (CP3ER) to improve sample efficiency. CP3ER achieves new state-of-the-art (SOTA) performance in 21 tasks across DeepMind control suite and Meta-world. To our knowledge, CP3ER is the first method to apply diffusion/consistency models to visual RL and demonstrates the potential of consistency models in visual RL. More visualization results are available at https://jzndd.github.io/CP3ER-Page/.
Cite
@article{arxiv.2410.00051,
title = {Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization},
author = {Haoran Li and Zhennan Jiang and Yuhui Chen and Dongbin Zhao},
journal= {arXiv preprint arXiv:2410.00051},
year = {2024}
}
Comments
Accepted at the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS2024)