Model Based Deep Reinforcement Learning

Model-based deep reinforcement learning (MBRL) aims to improve the sample efficiency and robustness of reinforcement learning agents by leveraging learned or provided models of the environment's dynamics. Current research focuses on enhancing model accuracy and stability, particularly addressing challenges like multimodal uncertainty and efficient adaptation to environmental changes, often employing techniques like ensemble methods, incremental model learning, and integration with imitation learning. MBRL's significance lies in its potential to enable faster and safer training of agents for complex tasks in robotics, control systems, and other domains where extensive real-world interaction is impractical or risky.