Learning Time-Optimal Control of Gantry Cranes

The paper presents an experimental study on the application of deep reinforcement learning (DRL) methods to the problem of optimally transporting cargo loads by an overhead gantry crane in minimal time. Experiments in simulation using a physics engine on two versions of the problem, with two and four degrees of freedom and employing reward functions that reflect the objective of load stabilization in minimal time, demonstrate that policies trained with the Stochastic Actor Critic (SAC) DRL method achieve up to 20% shorter transport time in comparison with controllers designed by means of more traditional methods from the field of control engineering.