arXiv Artificial Intelligence

Meta-RL with Bayesian Linear Task Models

1 hour ago 11.09.2026 07:00 👁 1 views
Meta-RL with Bayesian Linear Task Models

Quick summary

arXiv:2512.20974v4 Announce Type: replace-cross Abstract: Deep Bayesian reinforcement learning adapts to unseen tasks by inferring latent transition and reward models, but existing methods typically rely on variational posteriors and evidence lower bounds, introducing approximation error and unstable task representations. We introduce GLiBRL, a deep Bayesian RL framework that combines generalised linear task models with learnable non-linear basis functions. GLiBRL features conjugate Bayesian inference, yielding exact, sequential posterior updates over task parameters and model noise, together

Key takeaways

  • arXiv:2512.20974v4 Announce Type: replace-cross Abstract: Deep Bayesian reinforcement learning adapts to unseen tasks by inferring latent transition and reward models, but existing methods typically rely on variational posteriors and evidence lower bounds, introducing approximation error and unstable task representations.
  • We introduce GLiBRL, a deep Bayesian RL framework that combines generalised linear task models with learnable non-linear basis functions.
  • GLiBRL features conjugate Bayesian inference, yielding exact, sequential posterior updates over task parameters and model noise, together

Why it matters

“Meta-RL with Bayesian Linear Task Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗