Diffusion Policies with Value-Conditional Optimization for Offline Reinforcement Learning

The recent submission of 'Diffusion Policies with Value-Conditional Optimization for Offline Reinforcement Learning' on arXiv introduces DIVO, a method designed to tackle the challenge of value overestimation in offline reinforcement learning. This issue arises from out-of-distribution actions that hinder policy performance. DIVO leverages diffusion models to produce high-quality, in-distribution state-action samples, which facilitates efficient policy improvement. By implementing a binary-weighted mechanism that focuses on high-advantage actions, DIVO not only enhances the alignment with the dataset's distribution but also ensures a critical balance between conservatism and explorability. Evaluated on the D4RL benchmark, DIVO shows promise in improving policy performance by dynamically filtering actions with high return potential, marking a significant step forward in the field of reinforcement learning.