CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments

28 Jan 2019Pierre FournierOlivier SigaudCédric ColasMohamed Chetouani

In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions. We argue that this setting defines a realistic scenario and we present a generic discrete-state discrete-action model of such environments... (read more)

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