arXiv AI September 11, 2026 By Jingyang You, Hanna Kurniawati

Meta-RL with Bayesian Linear Task Models

The paper introduces GLiBRL, a deep Bayesian reinforcement learning framework that uses generalized linear task models with learnable nonlinear basis functions. GLiBRL performs exact, sequential Bayesian inference without variational approximations, providing closed‑form posterior updates and a marginal likelihood. The method is permutation‑invariant, compatible with both off‑ and on‑policy algorithms, and achieves the best zero‑shot performance among eight meta‑RL baselines on MuJoCo locomotion and MetaWorld manipulation tasks.

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