Multi Step Off Policy

Multi-step off-policy reinforcement learning aims to improve the efficiency of learning from data collected by different policies over multiple time steps, addressing the limitations of single-step methods. Current research focuses on developing algorithms that mitigate the high variance associated with importance sampling and the underestimation of value functions inherent in some multi-step approaches, exploring techniques like "highway gates" and doubly multi-step methods within actor-critic frameworks. These advancements are significant because they enable more efficient learning from complex, delayed-reward environments, improving performance in applications such as video games and potentially other domains requiring sequential decision-making.