Shuvaev, S, Starosta, S, Kvitsiani, D, Kepecs, A, Koulakov, A (January 2020) R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making. In: NeurIPS 2020.
Abstract
When should you continue with your ongoing plans and when should you instead decide to pursue better opportunities? We show in theory and experiment that such stay-or-leave decisions are consistent with deep R-learning both behaviorally and neuronally. Our results suggest that real-world agents leave depleting resources when their reward rate falls below its exponential average, which, we argue, is a Bayes optimal rule in dynamic natural environments. Our work links reinforcement learning, the marginal value theorem and Bayesian inference approaches to offer a learning algorithm and a decision rule for making sequential stay-or-leave choices.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | bioinformatics > computational biology organism description > animal behavior > decision making |
| CSHL Authors: | |
| Communities: | CSHL labs > Kepecs lab CSHL labs > Koulakov lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 1 January 2020 |
| Date Deposited: | 30 Jun 2021 13:23 |
| Last Modified: | 30 Jun 2021 13:23 |
| URI: | https://repository.cshl.edu/id/eprint/40237 |
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