One-shot learning and behavioral eligibility traces in sequential decision making.

ABSTRACT: In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one-shot). Here, we show one-shot learning of sequences. We developed a novel paradigm to directly observe which actions and states along a multi-step sequence are reinforced after a single reward. By focusing our analysis on those states for which RL with and without elig

SUBMITTER: Lehmann MP 

PROVIDER: S-EPMC6897511 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature