Accelerating Quantum Reinforcement Learning With A Quantum Natural Policy Gradient Based Approach

Abstract

We address the problem of quantum reinforcement learning (QRL) under model-free settings with quantum oracle access to the Markov Decision Process (MDP). This paper introduces a Quantum Natural Policy Gradient (QNPG) algorithm, which replaces the random sampling used in classical Natural Policy Gradient (NPG) estimators with a deterministic gradient estimation approach, enabling seamless integration into quantum systems. While this modification introduces a bounded bias in the estimator, the bias decays exponentially with increasing truncation levels. This paper demonstrates that the proposed QNPG algorithm achieves a sample complexity of \(\tilde\{\mathcal\{O\}\}(\epsilon^\{-1.5\})\) for queries to the quantum oracle, significantly improving the classical lower bound of \(\tilde\{\mathcal\{O\}\}(\epsilon^\{-2\})\) for queries to the MDP.

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