Bridging State and History Representations: Understanding Self-Predictive RL

arXiv:2401.08898v2 Announce Type: replace
Abstract: Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions …

abstract arxiv core cs.ai cs.lg decision frameworks history however markov observable predictive processes reinforcement reinforcement learning relationships representation representation learning state type understanding