Improving Experience Replay with Successor Representation

TL;DR

This paper introduces a novel experience replay prioritization method that combines gain and need, inspired by neuroscience, leading to improved reinforcement learning performance in benchmarks like Atari games.

Contribution

The paper proposes a new algorithm that incorporates both gain and need into experience replay prioritization, enhancing learning efficiency.

Findings

Significant performance improvements in Dyna-Q maze

Enhanced results on Atari game benchmarks

Effective integration of neuroscience insights into RL algorithms

Abstract

Prioritized experience replay is a reinforcement learning technique whereby agents speed up learning by replaying useful past experiences. This usefulness is quantified as the expected gain from replaying the experience, a quantity often approximated as the prediction error (TD-error). However, recent work in neuroscience suggests that, in biological organisms, replay is prioritized not only by gain, but also by "need" -- a quantity measuring the expected relevance of each experience with respect to the current situation. Importantly, this term is not currently considered in algorithms such as prioritized experience replay. In this paper we present a new approach for prioritizing experiences for replay that considers both gain and need. Our proposed algorithms show a significant increase in performance in benchmarks including the Dyna-Q maze and a selection of Atari games.