Skill emergence and transfer in multi-agent environments

A central problem in training artificial agents to perform complex skills is specifying appropriate cost functions whose optimization will lead to the desired behavior. Specifying detailed cost functions is laborious and often inefficient. The training of agents in competitive and cooperative multi-agent environments provides an avenue to circumvent these limitations: By competition and cooperation agents provide to each other a natural curriculum that can lead to the emergence of complicated skills, even if the rewards of the multi-agent game are simple [1].