Model Based Policy

Model-based policy optimization in reinforcement learning aims to improve the sample efficiency and robustness of learning control policies by leveraging learned models of the environment's dynamics. Current research focuses on refining model architectures (e.g., symbolic models, neural networks), addressing uncertainty propagation within these models (e.g., through moment matching), and developing efficient policy search methods (e.g., policy-space search, critic-guided reuse). These advancements enhance the applicability of reinforcement learning to complex real-world scenarios where data acquisition is costly or limited, with implications for robotics, autonomous systems, and other domains requiring adaptive control.