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Enhancing intelligent agents with episodic memory
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This paper brings together work in modeling episodic memory and reinforcement learning (RL). We demonstrate that is possible to learn to use episodic memory retrievals while simultaneously learning to act in an external environment. In a series of three experiments, we investigate using RL to learn what to retrieve from episodic memory and when to retrieve it, how to use temporal episodic memory retrievals, and how to build cues that are the conjunctions of multiple features. In these experiments, our empirical results demonstrate that it is computationally feasible to learn to use episodic memory; furthermore, learning to use internal episodic memory accomplishes tasks that reinforcement learning alone cannot. These experiments also expose some important interactions that arise between reinforcement learning and episodic memory. In a fourth experiment, we demonstrate that an agent endowed with a simple bit memory cannot learn to use it effectively. This indicates that mechanistic characteristics of episodic memory may be essential to learning to use it, and that these characteristics are not shared by simpler memory mechanisms.