Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance

arXiv:2605.30056v1 Announce Type: cross Abstract: Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approaches, one representative branch focuses on the sampling-based policy optimization. This design enables better exploration capability of the diffusion model, particularly at the beginning of training, but suffer from low exploitation in Q-value information, resulting in a slow policy convergence. Another branch pays attention to gradient-based policy optimization, which s

Source: arXiv cs.LG — read the full report at the original publisher.

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