Distributional Active Inference

2026-01-28

AI Summary

This work addresses the sample inefficiency of reinforcement learning in complex environments, which often stems from inadequate state perception. To this end, the paper introduces a formal abstraction framework that seamlessly integrates active inference into distributional reinforcement learning, enabling joint optimization of perception and decision-making. Notably, the proposed approach unifies model-based, model-free, and distributional reinforcement learning paradigms without requiring an explicit model of the environment’s transition dynamics. Experimental results demonstrate that, without relying on environmental dynamics models, the method substantially improves sample efficiency while achieving near-optimal control performance.

Technology Category

• Machine Learning: Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning. Because the reinforcement learning framework addresses only the latter, it tends to deliver sample-inefficient solutions. Active inference is the state-of-the-art process theory that explains how biological brains handle this dual problem. However, its applications to artificial intelligence have thus far been limited to extensions of existing model-based approaches. We present a formal abstraction of reinforcement learning algorithms that spans model-based, distributional, and model-free approaches. This abstraction seamlessly integrates active inference into the distributional reinforcement learning framework, making its performance advantages accessible without transition dynamics modeling.

Problem

• active inference
• reinforcement learning
• distributional reinforcement learning
• sample efficiency
• sensory state organization

Innovation

• Active Inference
• Distributional Reinforcement Learning
• Sample Efficiency
• Unified Abstraction
• Model-Free Control