  In the case of the two-person zero-sum 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 multi-agent 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, model-based
prediction and parallel search. Then, we use the deep q-learning method in
reinforcement learning 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 by
the RoboCup Small Size League robots passing collaboration.
