Haruto Yamamoto

Dataset-Constrained Offline Reinforcement Learning with Value Memory and Preference Feedback

A novel framework that integrates value memory architectures with preference-based feedback mechanisms within a dataset-constrained optimization landscape is introduced, demonstrating that this dual-mechanism framework significantly improves both policy stability and asymptotic performance.