摘要
In this paper we describe a new reinforcement learning approach based on different states. When the multiagent is in coordination state,we take all coordinative agents as players and choose the learning approach based on game theory. When the multiagent is in indedependent state,we make each agent use the independent learning. We demonstrate that the proposed method on the pursuit-evasion problem can solve the dimension problems induced by both the state and the action space scale exponentially with the number of agents and no convergence problems,and we compare it with other related multiagent learning methods. Simulation experiment results show the feasibility of the algorithm.
In this paper we describe a new reinforcement learning approach based on different states. When the muhiagent is in coordination state, we take all coordinative agents as players and choose the learning approach based on game theory. When the muhiagent is in indedependent state, we make each agent use the independent learning. We demonstrate that the proposed method on the pursuit-evasion problem can solve the dimension problems induced by both the state and the action space scale exponentially with the number of agents and no convergence problems, and we compare it with other related muhiagent learning methods. Simulation experiment results show the feasibility of the algorithm.
