A Diffusion Model-Based Approach to Active Inference

One of the current challenges in machine learning is the acquisition of generative models to enable decision making in complex, uncertain environments. In Bayesian methods like Active Inference, this requires the estimation of posterior distributions, which is often intractable. Variational Free Energy (VFE) provides a tractable approximation method for this estimation. This paper proposes a novel framework that leverages the expressive power of diffusion models to estimate free energy quickly and efficiently. Our approach, merging concepts from statistical physics, machine learning, and computational neuroscience, aims to improve the estimation of posterior distributions. We establish a theoretical link between active inference and diffusion models, emphasizing their shared strategies for uncertainty management, action selection, and model optimization. We propose using diffusion processes to approximate environmental generative models and employ score-based guidance for action selection through expected free-energy minimization. Simulations in increasingly complex grid world environments demonstrate the framework's ability to handle partial observability and stochastic transitions, showing enhanced goal-directed behavior and uncertainty resolution compared to traditional methods. This suggests a path toward scalable active inference agents that could reason about uncertainty in real-world contexts, with possible applications to robotics, healthcare, and adaptive control systems.