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

arXiv: https://arxiv.org/abs/2502.21057 · HTML: https://mlsanity.com/paper/2502.21057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control](https://mlsanity.com/paper/2103.02142/) (2021)
- [Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1811.11329/) (2018)
- [Learning H-Infinity Locomotion Control](https://mlsanity.com/paper/2404.14405/) (2024)
- [Robust Deep Reinforcement Learning for Quadcopter Control](https://mlsanity.com/paper/2111.03915/) (2021)

## Built on by (most cited first, 1 of 1)

- [Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning](https://mlsanity.com/paper/2603.12110/) (2026)

## 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.