A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework.

ABSTRACT: This paper offers a formal account of policy learning, or habitual behavioral optimization, under the framework of Active Inference. In this setting, habit formation becomes an autodidactic, experience-dependent process, based upon what the agent sees itself doing. We focus on the effect of environmental volatility on habit formation by simulating artificial agents operating in a partially observable Markov decision process. Specifically, we used a "two-step" maze paradigm, in which the agent has to decide whether to go left or right to secure a reward. We observe that in volatile environments with numerous reward locations, the agents learn to adopt a generalist strategy, never forming a strong habitual behavior for any preferred maze direction. Conversely, in conservative or static envir

SUBMITTER: Chen AG 

PROVIDER: S-EPMC7861269 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature