Nov 6, 2025 · This approach combines model learning, data generation and policy learning in an iterative process. The agent learns a model of the environment and uses it to generate …

This page provides a comprehensive overview of Model-Based Reinforcement Learning (MBRL), covering foundational concepts, key methodologies, and modern algorithms.

3 days ago · This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class …

ed RL? What is a model.

Model-based reinforcement learning (MBRL) follows the framework of an agent interacting in an environment, learning a model of said environment, and then **leveraging the model for …

Nov 28, 2025 · Here, authors introduce SINDy-RL, a framework that combines sparse system identification with reinforcement learning to yield efficient, interpretable, and high-performing …

In this section, we will implement Dyna-Q, one of the simplest model-based reinforcement learning algorithms. A Dyna-Q agent combines acting, learning, and planning. The first two …

Dec 2, 2022 · This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end …

Dec 1, 2024 · In model-based RL, instead of learning the optimal policy purely by interacting with the environment, we first learn a model of the environment’s dynamics. This model predicts …

Explore a comprehensive and practical approach to reinforcement learning. Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs …