A Self-Organized Fuzzy-Neuro Reinforcement Learning System for Continuous State Space for Autonomous Robots

摘要

This paper proposes the system that combines self-organized fuzzy-neural networks with reinforcement learning system (Q-learning, stochastic gradient ascent : SGA) to realize the autonomous robot behavior learning for continuous state space. The self-organized fuzzy neural network works as adaptive input state space classifier to adapt the change of environment, the part of reinforcement learning has the learning ability corresponding to rule for the input state space . Simultaneously, to simulate the real environment the robot has ability to estimate own-position. Finally, it is clarified that our proposed system is effective through the autonomous robot behavior learning simulation by using the khepera robot simulator.