Unsupervised Representation Learning in Deep Reinforcement Learning: A Review

arXiv:2208.14226v3 Announce Type: replace
Abstract: This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguous, high-dimensional, and complex to interpret, many dynamical systems can be effectively described by a low-dimensional set of state variables. Discovering these state variables from the data is a crucial aspect for (i) improving the data efficiency, robustness, and generalization of DRL methods, (ii) tackling the curse of dimensionality, …

abstract arxiv context cs.lg data low measurement reinforcement reinforcement learning representation representation learning review set state systems type unsupervised variables while