Multilayered reinforcement learning for complicated collision avoidance problems

We have proposed the collision avoidance methods in a multirobot system based on the information exchanged by the "LOCISS: Locally Communicable Infrared Sensory System", which is developed by the authors. One of the problems in the LOCISS based methods is that the number of situations which should be considered increases very much when the number of the robots and stationary obstacles in the working environment increases. In order to reduce the required computational power and memory capacity for such a large number of situations, we propose, in this paper, a multilayered reinforcement learning scheme to acquire appropriate collision avoidance behaviors. The feasibility and the performance of the proposed scheme is examined through the experiment using actual mobile robots.