Adaptive parameter sharing for multi-agent reinforcement learning
CoRR(2023)
摘要
Parameter sharing, as an important technique in multi-agent systems, can
effectively solve the scalability issue in large-scale agent problems. However,
the effectiveness of parameter sharing largely depends on the environment
setting. When agents have different identities or tasks, naive parameter
sharing makes it difficult to generate sufficiently differentiated strategies
for agents. Inspired by research pertaining to the brain in biology, we propose
a novel parameter sharing method. It maps each type of agent to different
regions within a shared network based on their identity, resulting in distinct
subnetworks. Therefore, our method can increase the diversity of strategies
among different agents without introducing additional training parameters.
Through experiments conducted in multiple environments, our method has shown
better performance than other parameter sharing methods.
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关键词
Multi-agent reinforcement learning,parameter sharing,scalability
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