Researchers have introduced JAXbounce, a new benchmark designed to test active inference agents in continuous control environments that extend beyond simple, low-dimensional settings. This benchmark features continuous states, stochastic dynamics, and flexible observation models that can represent environments as state vectors or pixel grids, with various noise conditions. Experiments using JAXbounce demonstrated that agents with state-based active inference and neural network perception could exhibit preference-oriented behavior even under increasing uncertainty, providing a reproducible platform for active inference research. AI
IMPACT Provides a new, reproducible benchmark for evaluating active inference agents in complex, continuous control environments.
RANK_REASON The item describes a new benchmark for active inference research published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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