Control (management)

Process (computing)

Engineering

Aerospace engineering

Biology

Statistics

Markov process

Marine engineering

Control engineering

Unmanned surface vehicle

Operating system

Computer science

Markov decision process

Controller (irrigation)

Surface (topology)

Reinforcement learning

Simulation

Mathematics

Agronomy

Path (computing)

Artificial intelligence

Geometry

Structural engineering

Programming language

Control theory (sociology)

Reinforcement

Q-learning

Ocean Engineering

In this paper, a deep reinforcement learning (DRL)-based controller for path following of an unmanned surface vehicle (USV) is proposed. The proposed controller can self-develop a vehicle’s path following capability by interacting with the nearby environment. A deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic-based reinforcement learning algorithm, was adapted to capture the USV’s experience during the path-following trials. A Markov decision process model, which includes the state, action, and reward formulation, specially designed for the USV path-following problem is suggested. The control policy was trained with repeated trials of path-following simulation. The proposed method’s path-following and self-learning capabilities were validated through USV simulation and a free-running test of the full-scale USV.

Deep reinforcement learning-based controller for path following of an unmanned surface vehicle