标题： 脆弱、稳健和反脆弱：压力下强化学习中参数响应的视角 显示英文标题

标题： Fragile, Robust, and Antifragile: A Perspective from Parameter Responses in Reinforcement Learning Under Stress

摘要： 本文通过系统分析在内部和外部压力下的网络参数，探讨了强化学习（RL）策略的鲁棒性。 受神经科学中突触可塑性的启发，突触过滤通过选择性地扰动参数来引入内部压力，而对抗攻击则通过修改智能体观察来施加外部压力。 这种双重方法使参数可以根据其在干净和对抗环境中的策略性能影响被分类为脆弱、鲁棒或反脆弱。 定义了参数得分以量化这些特性，并在Mujoco连续控制环境中对PPO训练的智能体进行了框架验证。 结果表明存在在压力下能增强策略性能的反脆弱参数，证明了有针对性的过滤技术可以提高RL策略的适应性。 这些见解为未来设计稳健和反脆弱的RL系统的进步提供了基础。 显示英文摘要

摘要： This paper explores Reinforcement learning (RL) policy robustness by systematically analyzing network parameters under internal and external stresses. Inspired by synaptic plasticity in neuroscience, synaptic filtering introduces internal stress by selectively perturbing parameters, while adversarial attacks apply external stress through modified agent observations. This dual approach enables the classification of parameters as fragile, robust, or antifragile, based on their influence on policy performance in clean and adversarial settings. Parameter scores are defined to quantify these characteristics, and the framework is validated on PPO-trained agents in Mujoco continuous control environments. The results highlight the presence of antifragile parameters that enhance policy performance under stress, demonstrating the potential of targeted filtering techniques to improve RL policy adaptability. These insights provide a foundation for future advancements in the design of robust and antifragile RL systems.