Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

ICLR 2020 Anirudh GoyalShagun SodhaniJonathan BinasXue Bin PengSergey LevineYoshua Bengio

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-level meta-policy that triggers the appropriate behaviors for a given situation... (read more)

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