Cooperative multi-agent reinforcement learning based on quantum computing
Xiangping Meng · Computer Engineering and Applications Journal · 2008
Due to the interactions among the agents in the cooperative multi-agent systems,multi-agent learning problem complexity can rise rapidly with the number of agents or their behavioral sophistication.In order to converge to desirable equilibrium,agents generally require sufficient exploration of strategy space and coordinate their policies to achieve optimal equilibrium.A novel cooperative multi-agent learning method is proposed based on quantum theory and reinforcement learning.This method not only coordinates agents’ behaviors using quantum entanglement and helps agents make action selection under quantum superposition,but also adopts Grover’s searching algorithm which can speed up learning.This method also makes a good tradeoff between exploration and exploitation using probability characteristics of quantum theory.The results of simulated experiments show that quantum theory can be effectively used to cooperative multi-agent reinforcement learning.