Generalizing Inverse Kinematics for Representation Learning to Finite Memory Partially Observable Markov Decision Processes

This work generalizes inverse kinematics techniques to learn agent-centric state representations in high-dimensional, finite-memory Partially Observable Markov Decision Processes (FM-POMDPs).