Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
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 sufficiently exploits the gradient of the Q function yet tends to collapse into a unimodal policy with low diversity. To address this issue, we prop...
| Authors | Shutong Ding, Zejia Zhong, Zhongyi Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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