A MultiLayer Architecture for Cooperative MultiAgent Systems In multiagent cooperative systems with valued based reinforcement learning, agents learn how to complete the task by an optimal policy learned through valuepolicy improvement iterations. But how to design a policy that avoids social dilemmas and come to a common consensus between agents is an important issue. A method that increases the success rate of cooperation by assessing the cooperative tendency is proposed in this article. The method learns the rules of cooperation by recording cooperation probabilities for agents in a Layered Cooperation Model (LCM). Probabilities are considered as a base while agents are making a decision beneficial to all through the game theory. The method is tested using two cooperative tasks. The results show that the proposed algorithm, which addresses the instability and ambiguity in the winorlearningfast and policy hillclimbing method (WoLFPHC) and requires significantly less memory space than the Nash Bargaining Solution (NBS), is more stable and more efficient than other methods.

Multiagent Collaboration for Feasible Collaborative Behavior Construction and Evaluation * In the case of the twoperson zerosum stochastic game with a central controller, this paper proposes a best collaborative behavior search and selection algorithm based on reinforcement learning, in response to how to choose the best collaborative object and action for the central controller. In view of the existing multiagent collaboration and confrontation reinforcement learning methods, the methods of traversing all actions in a certain state leads to the problem of long calculation time and unsafe policy exploration. This paper proposes to construct a feasible collaborative behavior set by using action space discretization, establishing models of both sides, modelbased prediction and parallel search. Then, we use the temporal difference learning method to train the scoring function to select the optimal collaboration behavior from the feasible collaborative behavior set. This method enables efficient and accurate calculation in an environment with strong confrontation, high dynamics and a large number of agents, which is verified in the RoboCup Small Size League robots ball passing collaboration.