Generalizing Multi-Step Inverse Models for Representation Learning to
Finite-Memory POMDPs
Generalizing Multi-Step Inverse Models for Representation Learning to
Finite-Memory POMDPs
Discovering an informative, or agent-centric, state representation that encodes only the relevant information while discarding the irrelevant is a key challenge towards scaling reinforcement learning algorithms and efficiently applying them to downstream tasks. Prior works studied this problem in high-dimensional Markovian environments, when the current observation may be a complex …