Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties

2020-03-30 · Qingrui Zhang, Wei Pan, Vasso Reppa

This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional control method with deep reinforcement learning. With the conventional control, we can ensure the learning-based control law provides closed-loop stability for the overall system, and potentially increase the sample efficiency of the deep reinforcement learning. With the reinforcement learning, we can directly learn a control law to compensate for modeling uncertainties. In the proposed control, a nominal system is employed for the design of a baseline control law using a conventional control approach. The nominal system also defines the desired performance for uncertain autonomous vehicles to follow. In comparison with traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm via extensive simulation results.

📄 PDF Abstract BibTeX arXiv:2003.13839

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous VehiclesDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

2020-08-17 · Qingrui Zhang, Wei Pan, Vasso Reppa

This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines…

Collision Avoidance Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles

2021-06-16 · WenQi Cai, Arash B. Kordabad, Hossein N. Esfahani, Anastasios M. Lekkas 외

In this work, we propose a Model Predictive Control (MPC)-based Reinforcement Learning (RL) method for Autonomous Surface Vehicles (ASVs). The objective is to find an optimal policy that minimizes the closed-loop perform…

Model Predictive Control Position reinforcement-learning Reinforcement Learning (RL)

Nonplanar Model Predictive Control for Autonomous Vehicles with Recursive Sparse Gaussian Process Dynamics

2026-02-18 · Ahmad Amine, Kabir Puri, Viet-Anh Le, Rahul Mangharam arxiv

This paper proposes a nonplanar model predictive control (MPC) framework for autonomous vehicles operating on nonplanar terrain. To approximate complex vehicle dynamics in such environments, we develop a geometry-aware m…

Autonomous Vehicles

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

2026-07-02 · Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran arxiv

Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajecto…

Reinforcement Learning

Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles

2020-01-12 · Il Bae, Jaeyoung Moon, Junekyo Jhung, Ho Suk 외

This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose a…