This article introduces Affective Replay with M2MR Integration, a novel experience replay mechanism for reinforcement learning that combines emotion-driven prioritization with synthetic memory generation. Unlike traditional experience replay methods that rely on static priorities or uniform sampling, our hybrid approach dynamically assesses experience relevance through emotion-based weighting while augmenting the replay buffer with high-confidence synthetic experiences generated from emotionally similar memories. The approach is motivated by the challenges of applying reinforcement learning in financial environments characterized by regime shifts and non-stationary data. Evaluated on market-based simulations using real S&P 500 and VIX data, Affective Replay achieves consistent and statistically significant performance improvements over all baselines, including combined experience replay, memory-to-memory replay (M2MR), variance-aware continuous learning, and prioritized experience replay. Our results suggest that improving how reinforcement learning systems prioritize and reuse historical observations may enhance model adaptability in financial applications.
Joseph Simonian (Sat,) studied this question.