[4O3-J-7] Agents: learning agents

[4O3-J-7-02] Evolution of Subjective Utilities by GA with BLX-α

〇Naoya Okada¹, Koichi Moriyama¹, Atsuko Mutoh¹, Tohgoroh Matsui², Nobuhiro Inuzuka¹ (1. Nagoya Institute of Technology, 2. Chubu University)

Keywords:Genetic Algorithm, Multi-agent system, Reinforcement Learning

Utility-based Q-learning, which uses subjective utilities as rewards of Q-learning, has been proposed and the utilities that derive mutual cooperation in a Prisoner's Dilemma game have been successfully evolved by real-coded genetic algorithm (RCGA). However, in that work, the genes were simply exchanged in the evolution process like a bit-string GA and the search space was not so wide as a result. This work investigates the evolution of the subjective utilities by RCGA with blend crossover (BLX-α) that has a powerful search ability by generating various chromosomes.