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One of the biggest challenges in multi-agent reinforcement learning iscoordination, a typical application scenario of this is traffic signal control.Recently, it has attracted a rising number of researchers and has become a hotresearch field with great practical significance. In this paper, we propose anovel method called MetaVRS~(Meta Variational RewardShaping) for traffic signalcoordination control. By heuristically applying the intrinsic reward to theenvironmental reward, MetaVRS can wisely capture the agent-to-agent interplay.Besides, latent variables generated by VAE are brought into policy forautomatically tradeoff between exploration and exploitation to optimize thepolicy. In addition, meta learning was used in decoder for faster adaptationand better approximation. Empirically, we demonstate that MetaVRS substantiallyoutperforms existing methods and shows superior adaptability, which predictablyhas a far-reaching significance to the multi-agent traffic signal coordinationcontrol.

Variationally and Intrinsically motivated reinforcement learning for decentralized traffic signal control