Neighboring crossover to improve GA-based Q-learning method for multi-legged robot control

Authors:
Tadahiko Murata;Masatoshi Yamaguchi
Affiliations:
Kansai University, Takatsuki, Osaka, Japan;Kansai University Graduate School, Takatsuki, Osaka, Japan
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Technical Note: \cal Q-Learning

Machine Learning
Reinforcement Learning

Reinforcement Learning

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Abstract

In this paper, we propose a crossover method to improve a GA-based Q-learning method for controlling multi-legged robots. As a GA-based Q-learning method, we employ a method called "Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)". We propose a crossover for QDSEGA, and a method to reward a robot in Q-learning in order to follow a moving target. Simulation results clearly show the effectiveness of the proposed methods.