Fast reinforcement learning approach to cooperative behavior acquisition in multi-agent system

摘要

In multi-agent robotic systems, the overlap of actions selected by each agent results in poor cooperation, while, at the same time, conventional reinforcement learning entails a large computational cost because every agent must learn. In this paper, we propose a novel method to solve these problems. The agent uses a rule set to conduct its behavior. Our system consists of reinforcement learning with an Action Selection Priority Level (ASPL) module and a generalized rules module. Using the ASPL, the reinforcement learning module chooses a proper cooperative behavior while the generalized rule module can accelerate the learning process. By applying the proposed method to robot soccer, the learning process can be accelerated by reducing the search space. The results of simulation and real experiments indicate the effectiveness of the proposed method.