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.

Policy Distillation and Value Matching in Multiagent Reinforcement Learning Multiagent reinforcement learning (MARL) algorithms have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information between agents via centralized critics to stabilize learning or through communication to improve performance, but do not generally consider how information can be shared between agents to address the curse of dimensionality in MARL. We posit that a multiagent problem can be decomposed into a multitask problem where each agent explores a subset of the state space instead of exploring the entire state space. This paper introduces a multiagent actorcritic algorithm for combining knowledge from homogeneous agents through distillation and valuematching that outperforms policy distillation alone and allows further learning in discrete and continuous action spaces.