Cranes control using multi-agent reinforcement learning

This paper deals with planning actions of the cranes in a coilyard of steel manufacture. Each crane would be operated independently but it must share the rail and be required single-track operation among the other cranes. And complete information around the coilyard is not always available to each operator of the crane. Sometimes operator does not need whole information, but there exist a complicated interaction among the cranes. There are two main problems in this case. One is an allocating generated tasks to a certain crane and the other is a controlling cranes' execution to avoid collision. We focus the latter one in this paper and we approach to acquire the cooperative rules to evade collision among the cranes which might be very difficult to design by any experts. Instead of hand-coding these rules, we apply profit-sharing, a kind of a reinforcement learning method, in our multi-agent model. And show that the performance of cranes which are operated by reinforced rules is better than that of cranes modelling by the reactive planner using hand-coded rules.