提出鲁棒强化学习算法，提升无人机抗干扰能力
Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control
- 将干扰抑制问题建模为对抗博弈，用确定性策略梯度训练鲁棒策略
- 在强干扰下实现高精度跟踪，无人机轨迹误差显著降低
- 适合需要高鲁棒性的飞行控制场景，尤其适用于复杂环境
本文提出一种鲁棒强化学习算法——鲁棒确定性策略梯度（RDPG），将H-infinity控制问题重新构建为用户与对抗者之间的二人零和动态博弈。该方法结合确定性策略梯度与深度强化学习，训练出能有效抑制干扰的鲁棒策略。其实际变体——鲁棒深度确定性策略梯度（RDDPG）引入双延迟更新机制，提升了训练稳定性与样本效率。在无人飞行器上的实验表明，在严重干扰条件下，该方法表现出优越的鲁棒性和轨迹跟踪精度。
原文摘要 · Abstract (English)
This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player zero-sum dynamic game between a user and an adversary. The method combines deterministic policy gradients with deep reinforcement learning to train a robust policy that attenuates disturbances efficiently. A practical variant, robust deep deterministic policy gradient (RDDPG), integrates twin-delayed updates for stability and sample efficiency. Experiments on an unmanned aerial vehicle demonstrate superior robustness and tracking accuracy under severe disturbance conditions.
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