Improving Experience Replay with Successor Representation

Prioritized experience replay is a reinforcement learning technique shown to speed up learning by allowing agents to replay useful past experiences more frequently. This usefulness is quantified as the expected gain from replaying the experience, and is often approximated as the prediction error (TD-error) observed during the corresponding experience. However, prediction error is only one possible prioritization metric. Recent work in neuroscience suggests that, in biological organisms, replay is prioritized by both gain and need. The need term measures the expected relevance of each experience with respect to the current situation, and more importantly, this term is not currently considered in algorithms such as deep Q-network (DQN). Thus, in this paper we present a new approach for prioritizing experiences for replay that considers both gain and need. We test our approach by considering the need term, quantified as the Successor Representation, into the sampling process of different reinforcement learning algorithms. Our proposed algorithms show a significant increase in performance in benchmarks including the Dyna-Q maze and a selection of Atari games.