The Missing Link Between Memory and Reinforcement Learning.

ABSTRACT: Reinforcement learning systems usually assume that a value function is defined over all states (or state-action pairs) that can immediately give the value of a particular state or action. These values are used by a selection mechanism to decide which action to take. In contrast, when humans and animals make decisions, they collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated. We have previously developed a model of memory processing that includes semantic, episodic and working memory in a comprehensive architecture. Here, we describe how this memory mechanism can support decision making when the alternatives cannot be evaluated based on immediate sensory information alone. Instead we first imagine, and then evaluate a poss

SUBMITTER: Balkenius C 

PROVIDER: S-EPMC7758424 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature