AI 中文总结
研究针对安全关键领域中深度强化学习的黑箱问题，利用策略蒸馏框架，以高性能TD3为教师模型，基于浅层决策树生成可解释学生代理，通过特定方法生成数据集，实现性能与教师相当并保持系统稳定性和可解释性。
AI 中文摘要
在机器人技术和汽车工程等安全关键领域，深度强化学习（DRL）的部署常因深度神经网络的黑箱性质受阻，缺乏透明度给监管合规和人机信任带来挑战。本文进行了一项实验研究，旨在使高性能连续控制DRL系统可解释。利用经典倒立摆基准实现了一个策略蒸馏框架，用高性能双延迟深度确定性策略梯度（TD3）智能体作为不透明的连续教师模型，将其策略蒸馏到基于浅层决策树的可解释学生代理中，并通过自定义物理感知特征和“噪声预言机展开”生成数据集，蒸馏过程实现了与专家教师相当的性能。比较控制理论分析揭示了基本权衡，仿真结果表明在为安全自主系统提供全局和局部可解释性的同时保持了有界输入有界输出（BIBO）稳定性。
英文摘要
In safety-critical sectors such as robotics and automotive engineering, the deployment of Deep Reinforcement Learning (DRL) is often hindered by the black-box nature of deep neural networks. This lack of transparency poses significant challenges for regulatory compliance and human-agent trust. This paper presents an experimental study aimed at making high-performance continuous control DRL systems interpretable. A policy distillation framework is implemented using the classic Inverted Pendulum benchmark. A high-performance Twin Delayed DDPG (TD3) agent serves as an opaque, continuous teacher model, whose policy is distilled into an interpretable student surrogate based on a shallow Decision Tree. By leveraging a custom physics-aware feature and "Noisy Oracle Rollouts" for dataset generation, the distillation process achieves performance equivalent to the expert teacher. Furthermore, comparative control theory analysis reveals a fundamental trade-off: transitioning from continuous to discrete rule-based control induces high-frequency Bang-Bang actuation and a stable bimodal limit cycle. Simulation results indicate that Bounded-Input Bounded-Output (BIBO) stability is maintained while providing both global and local interpretability for safe autonomous systems.
Comments6 pages, 7 figures
