Free Energy Projective Simulation (FEPS): Active inference with interpretability.

ABSTRACT: In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical models of perception and action. This effort is driven by a multidisciplinary interest in understanding aspects of self-organizing complex adaptive systems, including elements of agency. Various reinforcement learning (RL) models performing active inference have been proposed and trained on standard RL tasks using deep neural networks. Recent work has focused on improving such agents' performance in complex environments by incorporating the latest machine learning techniques. In this paper, we build upon these techniques. Within the constraints imposed by the FEP and AIF, we attempt to model agents in an interpret

SUBMITTER: Pazem J 

PROVIDER: S-EPMC12410762 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature