让强化学习环境动态变化，测试智能体实时适应能力
AbideGym: Turning Static RL Worlds into Adaptive Challenges
- 用动态扰动机制让训练环境实时变化
- 揭示静态策略在变化中失败的弱点
- 适合研究持续学习与鲁棒泛化的研究者
基于强化学习训练的智能体常因环境动态变化而失效，这一问题在静态基准测试中尤为突出。AbideGym 是一个动态的 MiniGrid 封装环境，引入了智能体感知的扰动机制和可扩展的复杂度设计，旨在促进智能体在单个实验过程中实现自适应。该框架通过暴露静态策略的脆弱性，推动模型在持续学习、课程学习和鲁棒泛化方面的进步，提供了一个模块化且可复现的评估体系。
原文摘要 · Abstract (English)
Agents trained with reinforcement learning often develop brittle policies that fail when dynamics shift, a problem amplified by static benchmarks. AbideGym, a dynamic MiniGrid wrapper, introduces agent-aware perturbations and scalable complexity to enforce intra-episode adaptation. By exposing weaknesses in static policies and promoting resilience, AbideGym provides a modular, reproducible evaluation framework for advancing research in curriculum learning, continual learning, and robust generalization.
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