Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active Inference

TL;DR

This paper presents a continual learning framework based on active inference, enabling agents to adapt and update their beliefs in dynamic environments without manual intervention, with applications in finance and healthcare.

Contribution

It introduces a novel discrete-time active inference approach for continual learning, deriving mathematical formulations and demonstrating practical self-learning agents in changing environments.

Findings

Agent can relearn and refine models efficiently

Framework applicable to complex domains like finance and healthcare

Active inference enables seamless exploration and exploitation

Abstract

Active inference is a mathematical framework for understanding how agents (biological or artificial) interact with their environments, enabling continual adaptation and decision-making. It combines Bayesian inference and free energy minimization to model perception, action, and learning in uncertain and dynamic contexts. Unlike reinforcement learning, active inference integrates exploration and exploitation seamlessly by minimizing expected free energy. In this paper, we present a continual learning framework for agents operating in discrete time environments, using active inference as the foundation. We derive the mathematical formulations of variational and expected free energy and apply them to the design of a self-learning research agent. This agent updates its beliefs and adapts its actions based on new data without manual intervention. Through experiments in changing environments, we demonstrate the agent's ability to relearn and refine its models efficiently, making it suitable for complex domains like finance and healthcare. The paper concludes by discussing how the proposed framework generalizes to other systems, positioning active inference as a flexible approach for adaptive AI.