Multiagent Learning Based on Black-board Model
Han Zhong-yuan · Jisuanji gongcheng · 2007
Q learning requires each state-action transform be visited infinitely,which limits its application when comes to large state-action space.This paper puts forward a black-board-model based multiagents cooperation learning algorithm.Agents cooperate and coordinate by a bull function which is defined in state-action space.By this bull function,agents can find those effective update more quickly and thus avoid those useless updates.Simulation proves the method can speed up the learning process at lower cost.