Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization

• URL: http://arxiv.org/abs/2410.00051v2
• Date: Tue, 29 Oct 2024 09:44:15 GMT
• Title: Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
• Authors: Haoran Li, Zhennan Jiang, Yuhui Chen, Dongbin Zhao,
• Abstract summary: We investigate the impact of non-stationary distribution and the actor-critic framework on consistency policy in online visual reinforcement learning. We 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.
• Score: 12.045972135237019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/.

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