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arXiv · 2108.12245

Active Inference for Stochastic Control

Aswin Paul·Noor Sajid·Manoj Gopalkrishnan·Adeel Razi
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Abstract

Active inference has emerged as an alternative approach to control problems given its intuitive (probabilistic) formalism. However, despite its theoretical utility, computational implementations have largely been restricted to low-dimensional, deterministic settings. This paper highlights that this is a consequence of the inability to adequately model stochastic transition dynamics, particularly when an extensive policy (i.e., action trajectory) space must be evaluated during planning. Fortunately, recent advancements propose a modified planning algorithm for finite temporal horizons. We build upon this work to assess the utility of active inference for a stochastic control setting. For this, we simulate the classic windy grid-world task with additional complexities, namely: 1) environment stochasticity; 2) learning of transition dynamics; and 3) partial observability. Our results demonstrate the advantage of using active inference, compared to reinforcement learning, in both deterministic and stochastic settings.

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Aswin Paul, Noor Sajid, Manoj Gopalkrishnan, Adeel Razi. 2021-08-27. Active Inference for Stochastic Control. https://doi.org/10.1007/978-3-030-93736-2_47

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