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Abstract: Many reinforcement learning exploration techniques are overly optimistic andtry to explore every state. Such exploration is impossible in environments withthe unlimited number of states. I propose to use simulated exploration with anoptimistic model to discover promising paths for real exploration. This reducesthe needs for the real exploration.



Autor: Ivo Danihelka

Fuente: https://arxiv.org/







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