Multiagent Reinforcement Learning Algorithm Research Based on Non Markov Environment
Xiangping Meng, Robert Babuška, Yu Chen, Lucian Buşoniu · 2006
In this paper several multiagent reinforcement learning algorithms are investigated, compared and analyzed. An effective reinforcement learning algorithm based on non Markov environment is proposed. This algorithm uses linear programming to find the best-response policy, and avoids solving multiple Nash equilibria problem. The algorithm involves simple procedures and easy computations, and can guarantee good learning convergence in some situations. Experiment results