Quantum Representation Learning for Reinforcement Learning in Robot Navigation

Abstract

Reinforcement learning for robot navigation requires representations that capture spatial geometry, long-horizon dependencies, and goal-conditioned structure. Classical neural networks provide flexible function approximators, but their representations are ultimately limited by classical feature transformations. We investigate quantum representation learning as a mechanism for encoding state–goal pairs in goal-conditioned reinforcement learning. We formulate navigation as a finite-horizon Markov decision process and introduce a quantum variational feature map that embeds continuous states into a high-dimensional Hilbert space. This representation is optimized jointly with a Q-learning objective, yielding a hybrid quantum–classical value function. Classical networks serve only as baselines to validate the environment and evaluation protocol. After correcting subtle issues in reward shaping and success detection, we observe that quantum representations produce coherent value structures, stable optimization dynamics, and competitive success rates relative to classical baselines. Rather than aiming to improve navigation performance, our objective is to provide a controlled and reproducible framework for analyzing how quantum feature geometry shapes value estimation and goal-conditioned behavior in offline RL. Our analysis highlights how quantum feature geometry influences value estimation and navigation behavior, establishing a reproducible foundation for studying quantum-enhanced representations in reinforcement learning for robotics.