From Tom Thumb to the Dockers: some experiments with foraging robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
The impact of diversity on performance in multi-robot foraging
Proceedings of the third annual conference on Autonomous Agents
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
Impacts of team size on role learning in multiagent systems
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Generation of roles in reinforcement learning considering redistribution of reward between agents
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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In learning agent team, giving appropriate amount of reward to each agent is necessary for division of labor. In this paper we present a novel approach for a reward allocation in which reward payments are conducted between agents. We consider foraging task in small maze as a test problem. Firstly, we investigate an algorithm in which exchanges of (fixed amount of) reward for a food are made between agents. The experimental results show that our approach can produce territorial division of labor and its performance is significantly improved than a global reinforcement approach in which all the agents obtain equally divided reward. Secondly, several extended algorithms in which agents can determine the price of a food on their own are investigated, it is shown that minimal "negotiations" between agents are effective for suitable price determination and good performances of teams.