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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,...
In this paper, we describe an architectural modification to Soar that gives a Soar agent the opportunity to learn statistical information about the past success of its actions and utilize this information when selecting an operator. This mechanism serves the same purpose as production utilities in ACT-R, but the implementation is more directly tied to the standard definition of the reinforcement learning...
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