  In multi-agent reinforcement learning, the cooperative learning behavior of
agents is very important. In the field of heterogeneous multi-agent
reinforcement learning, cooperative behavior among different types of agents in
a group is pursued. Learning a joint-action set during centralized training is
an attractive way to obtain such cooperative behavior, however, this method
brings limited learning performance with heterogeneous agents. To improve the
learning performance of heterogeneous agents during centralized training,
two-stage heterogeneous centralized training which allows the training of
multiple roles of heterogeneous agents is proposed. During training, two
training processes are conducted in a series. One of the two stages is to
attempt training each agent according to its role, aiming at the maximization
of individual role rewards. The other is for training the agents as a whole to
make them learn cooperative behaviors while attempting to maximize shared
collective rewards, e.g., team rewards. Because these two training processes
are conducted in a series in every timestep, agents can learn how to maximize
role rewards and team rewards simultaneously. The proposed method is applied to
5 versus 5 AI robot soccer for validation. Simulation results show that the
proposed method can train the robots of the robot soccer team effectively,
achieving higher role rewards and higher team rewards as compared to other
approaches that can be used to solve problems of training cooperative
multi-agent.

Artificial Intelligence