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Episodic memory is a psychology term which refers to the ability to recallspecific events from the past. We suggest one advantage of this particular typeof memory is the ability to easily assign credit to a specific state whenremembered information is found to be useful. Inspired by this idea, and theincreasing popularity of external memory mechanisms to handle long-termdependencies in deep learning systems, we propose a novel algorithm which usesa reservoir sampling procedure to maintain an external memory consisting of afixed number of past states. The algorithm allows a deep reinforcement learningagent to learn online to preferentially remember those states which are foundto be useful to recall later on. Critically this method allows for efficientonline computation of gradient estimates with respect to the write process ofthe external memory. Thus unlike most prior mechanisms for external memory itis feasible to use in an online reinforcement learning setting.

Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling