Title: All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

Abstract: Model-based reinforcement learning (MBRL) leverages a learned dynamics model to extract environmental information, offering promising solutions for longstanding challenges in robotics, particularly regarding data efficiency and safety. Nevertheless, the reliability of MBRL is often compromised because agents tend to exploit inaccuracies inherent in these learned dynamics models. To address this, we introduce a framework designed to manage the imprecision of probabilistic models by strategically handling uncertainty, thereby effectively preventing model exploitation. This paper highlights recent breakthroughs in direct hardware learning and safe exploration, while also outlining prospective avenues for the development of uncertainty-aware MBRL.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC