Optimal control of ship unloaders using reinforcement learning

This paper describes the use of Reinforcement Learning (RL) to the computation of time-optimal anti-swing control of a ship unloader. The unloading cycle has been divided into six subtasks and an optimization problem has been defined for each of them. A RL algorithm together with a multilayer perceptron neural network as a value function approximator have been used in the optimization. The results obtained are encouraging, since they reproduce a solution previously generated by using Optimal Control Theory.