Cooperative Behavior Acquisition by Learning and Evolution in a Multi-Agent Environment for Mobile Robots

Abstract

The objective of my research described in this dissertation is to realize learning and evolutionary methods for multiagent systems. This dissertation mainly consists of four parts. We propose a method that acquires the purposive behaviors based on the estimation of the state vectors in Chapter 3. In order to acquire the cooperative behaviors in multiagent environments, each learning robot estimates the Local Prediction Model (hereafter LPM) between the learner and the other objects separately. The LPM estimate the local in-teraction while reinforcement learning copes with the global interaction between multiple LPMs and the given tasks. Based on the LPMs which satisfies the Markovian environment assumption as possible, robots learn the desired behaviors using reinforcement learning. We also propose a learning schedule in order to make learning stable especially in the early stage of multiagent systems. Chapter 4 discusses how an agent can develop its behavior according to the complexity of the interactions with its environment. A method for controlling the complexity is