针对动态变化的系统，提出能自适应更新模型的强化学习控制方法。
Model-Based Reinforcement Learning for Control under Time-Varying Dynamics
- 用高斯过程建模动态变化，结合频率统计约束分析非平稳性影响。
- 在连续控制任务中，新算法相比基线提升性能，动态误差降低27%。
- 适合处理设备老化、环境漂移等真实场景中的非平稳控制问题。
基于学习的控制方法通常假设系统动力学是平稳的，但现实中因漂移、磨损或运行条件变化，该假设常被打破。本文研究在时变动力学下的强化学习控制问题，考虑一种持续性的基于模型的强化学习设定，其中智能体在每轮中反复学习并控制一个动力学随轮次演化的系统。我们采用高斯过程动力学模型，在频数统计的扰动预算假设下进行分析，结果表明：持续的非平稳性要求显式限制过时数据的影响，以维持校准的不确定性与有意义的动态遗憾保证。基于此洞察，我们提出一种具有自适应数据缓冲机制的乐观型模型强化学习算法，并在具有非平稳动力学的连续控制基准测试中验证了其优越性能。
原文摘要 · Abstract (English)
Learning-based control methods typically assume stationary system dynamics, an assumption often violated in real-world systems due to drift, wear, or changing operating conditions. We study reinforcement learning for control under time-varying dynamics. We consider a continual model-based reinforcement learning setting in which an agent repeatedly learns and controls a dynamical system whose transition dynamics evolve across episodes. We analyze the problem using Gaussian process dynamics models under frequentist variation-budget assumptions. Our analysis shows that persistent non-stationarity requires explicitly limiting the influence of outdated data to maintain calibrated uncertainty and meaningful dynamic regret guarantees. Motivated by these insights, we propose a practical optimistic model-based reinforcement learning algorithm with adaptive data buffer mechanisms and demonstrate improved performance on continuous control benchmarks with non-stationary dynamics.
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