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- 2017-10-12
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Description: Due to the advent of deep RL methods that allow the study of many agents in rich environments, multi-agent reinforcement learning has flourished in recent years. However, most of this work considers fully cooperative settings (Omidshafiei et al., 2017; Foerster et al., 2017a,b) and emergent
communication in particular (Das et al., 2017; Mordatch and Abbeel, 2017; Lazaridou, Peysakhovich, and Baroni, 2016; Foerster et al., 2016; Sukhbaatar, Fergus, and others, 2016). Considering future applications of multi-agent RL, such as self-driving cars, it is obvious that many of these will be
yEqual Contribution only partially cooperative and contain elements of competition and selfish incentives
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