Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 28 Feb 2025.
Builds on
11 of 30 references.
References · 11
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
- 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
- 2017Robust Adversarial Reinforcement Learningcited by 439
- 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 167
- 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 68
- 2018Deep Reinforcement Learning for Autonomous Drivingcited by 36
- 2024Learning H-Infinity Locomotion Controlcited by 9
- 2021Robust Deep Reinforcement Learning for Quadcopter Controlcited by 4
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Citing papers per year: 2026: 1.
1 paper.