中圖分類號：TP181 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.256451
中文引用格式： 安棟，王媛媛，宋寧寧，等. 強化學習評估指標的系統性分析與優化研究[J]. 電子技術應用，2025，51(10)：17-23.
英文引用格式： An Dong，Wang Yuanyuan，Song Ningning，et al. Systematic analysis and optimization research on reinforcement learning evaluation metrics[J]. Application of Electronic Technique，2025，51(10)：17-23.

Systematic analysis and optimization research on reinforcement learning evaluation metrics

Abstract： Reinforcement learning evaluation metrics, serving as core tools for measuring the performance of agents and guiding algorithm optimization, face key challenges such as the singularity of metrics, environmental dependence, and the lack of interpretability in practical applications. This paper systematically analyzes the classification framework of existing evaluation metrics, proposes a multi-dimensional metric system based on performance, learning process, strategy, robustness, and efficiency, and explores its applicability and limitations in different task scenarios (such as sparse reward and high-dimensional state space). The study indicates that traditional metrics are prone to overlooking the requirements of safety, efficiency, and alignment with human preferences in complex environments, and there is a need to design evaluation methods that integrate multiple objectives in combination with the characteristics of tasks. For future research, this paper suggests focusing on directions such as multi-objective Pareto optimization, reward modeling based on human feedback, and the quantification of exploration efficiency in sparse reward environments, so as to enhance the comprehensiveness and interpretability of evaluations. By combining theoretical analysis with practical cases, this paper provides methodological support for the standardization of the reinforcement learning evaluation system and its adaptation across different fields, thus promoting its efficient implementation in complex scenarios.

Key words : reinforcement learning；evaluation metrics；explainability；reward