Distributional Active Inference

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.

Machine Learning (cs.LG)

arXiv:2601.20985 [cs.LG]

(or arXiv:2601.20985v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.20985 Focus to learn more arXiv-issued DOI via DataCite

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