A NEW MULTI-AGENT REINFORCEMENT LEARNING ALGORITHM AND ITS APPLICATION TO MULTI-ROBOT COOPERATION TASKS

In multi-robot systems, joint-action must be employed to achieve cooperation because the evaluation to the behavior of a robot often depends on the other robots'behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this paper, a prediction-based reinforcement learning algorithm is presented for multi-robot cooperation tasks, which demands all robots to learn paper predict the probabilities of actions that other robots may execute. A multi-robot cooperation experiment is made to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation strategy much faster than the primitive reinforcement learning algorithm.