Proposal and evaluation of the penalty avoiding rational policy making algorithm with penalty level

Reinforcement learning (RL) is a kind of machine learning. It aims to adapt an agent to a given environment by utilizing a reward and a penalty. We know the penalty avoiding rational policy making algorithm (PARP) and the penalty avoiding profit sharing (PAPS) as examples of RL systems that are able to suppress a penalty and learn a rational policy. However they cannot treat multiple penalties. In this paper, we extend PARP/PAPS to the environments where there are some kinds of penalties. We propose the penalty avoiding rational policy making algorithm with penalty level (PARP_L) that can control how to avoid penalties. We show the effectiveness of PARP_L by soccer game simulations.