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Many multiagent systems in the real world include multiple types of agentswith different abilities and functionality. Such heterogeneous multiagentsystems have significant practical advantages. However, they also come withchallenges compared with homogeneous systems for multiagent reinforcementlearning, such as the non-stationary problem and the policy version iterationissue. This work proposes a general-purpose reinforcement learning algorithmnamed Heterogeneous League Training (HLT) to address heterogeneous multiagentproblems. HLT keeps track of a pool of policies that agents have exploredduring training, gathering a league of heterogeneous policies to facilitatefuture policy optimization. Moreover, a hyper-network is introduced to increasethe diversity of agent behaviors when collaborating with teammates havingdifferent levels of cooperation skills. We use heterogeneous benchmark tasks todemonstrate that (1) HLT promotes the success rate in cooperative heterogeneoustasks; (2) HLT is an effective approach to solving the policy version iterationproblem; (3) HLT provides a practical way to assess the difficulty of learningeach role in a heterogeneous team.

Learning Heterogeneous Agent Cooperation via Multiagent League Training