摘要
To solve the problem of conflict and deadlock with agents in multiagent system,an algorithm of multiagent coordination and cooperation was proposed. Taking agent in multiagent system as a player,the pursuit problem Markov model was built. The solution was introduced to get the optimal Nash equilibrium by multiagent reinforcement learning. The method of probability and statistics and Bayes formula was used to estimate the policy knowledge of other players. Relative mean deviation method was used to evaluate the confidence degree in order to increase the convergence speed. The simulation results on pursuit problem showed the feasibility and validity of the given algorithm.
To solve the problem of conflict and deadlock with agents in multiagent system,an algorithm of multiagent coordination and cooperation was proposed. Taking agent in multiagent system as a player,the pursuit problem Markov model was built. The solution was introduced to get the optimal Nash equilibrium by multiagent reinforcement learning. The method of probability and statistics and Bayes formula was used to estimate the policy knowledge of other players. Relative mean deviation method was used to evaluate the confidence degree in order to increase the convergence speed. The simulation results on pursuit problem showed the feasibility and validity of the given algorithm.
基金
Sponsored by the Fundamental Research Funds for the Central Universities of China (Grant No. DL12BB11)
Program for New Century Excellent Talentsin University (Grant No. NCET-10-0279)
Heilongjiang Postdoctoral Grant( Grant No. LRB11-334)
