Multi-agent reinforcement learning for crane control problem: designing rewards for conflict resolution

In recent years, a reinforcement learning approach to build an agent´s knowledge in a multi-agent world has prevailed when the reinforcement learning is applied to such a world, “a concurrent learning among the agents”, “a perceptual aliasing”, and “a designing rewards” are the most important problems to be considered. We have already confirmed that profit-sharing algorithm shows its robustness against these three problems through some experiments. In this paper, we focus on an advantage of profit-sharing compared to Q-learning through the simulations of controlling cranes where there exist the conflicts among the agents. The conflict resolution problem must become a bottle-neck in the multi-agent world if we approach to it by the top-down method. Similarly, Q-learning is also weak in this problem without exhaustive design of the rewards or detailed information about other agents. We present that profit-sharing method can be available to resolve it, through the results of some experiments on the controlling cranes problem