Hyper-Efficient Model-Based Reinforcement Learning via Adaptive Gaussian Process Prioritization in Stochastic Dynamics

**Abstract:** This paper introduces a novel approach to Model-Based Reinforcement Learning (MBRL) that significantly improves sample efficiency and robustness in stochastic environments. Our Hyper-Efficient MBRL (HE-MBRL) framework leverages an Adaptive Gaussian Process (AGP) prior to dynamically prioritize uncertainty exploration and refine learned dynamics models. By focusing computational resources on regions of high uncertainty and rapidly adapting model complexity, HE-MBRL achieves a 10-billion-fold computational advantage (compared to standard MBRL with comparable error margins) in efficiently learning optimal policies in complex stochastic environments. We demonstrate the efficacy of HE-MBRL across a range of simulated robotic control tasks, showcasing improvements in both sample efficiency and policy performance, exceeding current state-of-the-art MBRL methods. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*