Action Control of Autonomous Agents in Continuous Valued Space Using RFCN

摘要

Researchers on action control of autonomous agents and multiple agents have attracted increasing attention in recent years. The general methods using action control of agents are neural network, genetic programming, and reinforcement learning. In this study, we use neural network for action control of autonomous agents. Our method determines the structure and parameter of neural network in evolution. We proposed Flexibly Connected Neural Network (FCN) previously as a method of constructing arbitrary neural networks with optimized structures and parameters to solve unknown problems. FCN was applied to action control of an autonomous agent and showed experimentally that it is effective for perceptual aliasing problems. All of the experiments of FCN, however, are only in grid space. In this paper, we propose a new method based on FCN which can decide correction action in real and continuous valued space. The proposed method, called Real-valued FCN (RFCN), optimizes input-output functions of each unit, parameters of the input-output functions and speed of each unit. In order to examine its effectiveness, we applied the proposed method to action control of an autonomous agent to solve continuous-valued maze problems.