76 research outputs found
Learning Heterogeneous Agent Cooperation via Multiagent League Training
Many multiagent systems in the real world include multiple types of agents
with different abilities and functionality. Such heterogeneous multiagent
systems have significant practical advantages. However, they also come with
challenges compared with homogeneous systems for multiagent reinforcement
learning, such as the non-stationary problem and the policy version iteration
issue. This work proposes a general-purpose reinforcement learning algorithm
named as Heterogeneous League Training (HLT) to address heterogeneous
multiagent problems. HLT keeps track of a pool of policies that agents have
explored during training, gathering a league of heterogeneous policies to
facilitate future policy optimization. Moreover, a hyper-network is introduced
to increase the diversity of agent behaviors when collaborating with teammates
having different levels of cooperation skills. We use heterogeneous benchmark
tasks to demonstrate that (1) HLT promotes the success rate in cooperative
heterogeneous tasks; (2) HLT is an effective approach to solving the policy
version iteration problem; (3) HLT provides a practical way to assess the
difficulty of learning each role in a heterogeneous team
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