Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control

2025/02/28 by Lee, Taeho, Lee, Donghwan
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2502.21057

Abstract

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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