Quantum-Inspired Optimization for Reinforcement Learning Reward Shaping

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Reward shaping is a critical component in reinforcement learning (RL), aiming to guide agents towards desired behaviors by modifying the reward function. However, designing effective reward shaping functions can be a challenging and time-consuming process, often requiring significant domain expertise. This paper proposes a novel approach leveraging quantum-inspired optimization techniques to automate and improve the reward shaping process for RL agents. Specifically, we utilize amplitude estimation, a technique inspired by quantum computing, to iteratively refine reward functions based on agent interactions. The core idea is to treat the reward function as a parameter to be optimized using a quantum-inspired algorithm, effectively learning a reward function that best aligns with the agent's learning objective. We demonstrate, through theoretical analysis and a conceptual framework, the potential benefits of this approach in accelerating learning and improving the efficiency of RL algorithms. The proposed method offers a promising alternative to traditional reward shaping techniques, particularly in complex environments where designing hand-crafted reward functions is difficult. The evaluation of this approach is left for future work, focusing on practical implementation and comparative analysis with existing reward shaping strategies.

DOI: 10.5281/zenodo.22157166

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