Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update

ICLR 2018 Su Young LeeSungik ChoiSae-Young Chung

We propose Episodic Backward Update (EBU) - a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states... (read more)

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