Relational Forward Models for Multi-Agent Learning

ICLR 2019 Andrea TacchettiH. Francis SongPedro A. M. MedianoVinicius ZambaldiNeil C. RabinowitzThore GraepelMatthew BotvinickPeter W. Battaglia

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments... (read more)

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