AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Robustness of Optimality of Exploration Ratio against Agent Population in Multiagent Learning for Nonstationary Environments
Itsuki Noda

Last modified: 2014-06-18


In this article, I show the robustness of optimality of exploration ratioagainst the number of agents (agent population)under multiagent learning (MAL) situation in nonstationary environments.Agent population will affect efficiency of agents' learning becauseeach agent's learning causes noisy factors for others.From this point, exploration ratio should be small to make MAL effective.In nonstationary environments, on the other hand, each agent needs explore with enough probability to catch-upchanges of the environments.This means the exploration ratio need to be significantly large.I investigate the relation between the population and the efficiency ofexploration based on a theorem about relations betweenthe exploration ratio and a lower boundary of learning error.Finally, it is shown that the population of the agents does not affectthe optimal exploration ratio under a certain condition.This consequence is confirmed by several experimentsusing population games with various reward functions.


nonstationary environment; exploration tradeoff; agent population

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