Multiagent reinforcement learning method with an improved ant colony system

Multiagent reinforcement learning has gained increasing attention in recent years. The authors discuss coordination means for sharing episodes and sharing policies in the field of multiagent reinforcement learning. From the point of the view of reinforcement learning, we analyse the performance of indirect media communication among multi-agents on an ant colony system which is an efficient method that uses pheromones to solve optimization problems. Based on the above, we propose the Q-ACS method, modifying the global updating rule in ACS for learning agents to share better episodes benefited from the exploitation of accumulated knowledge. Meanwhile, taking the visited times into account, we propose T-ACS by presenting a state transition policy for learning agents to share better policies, benefiting from biased exploration. To demonstrate the coordination performance of learning agents in our methods, we conducted experiments on an optimization problem, the traveling salesman problem. Comparison of results with ACS, Q-ACS and T-ACS show that the improved methods are efficient for solving the optimization problem