Hybrid Policy

Hybrid policies combine the strengths of different control approaches, such as classical planning algorithms and machine learning methods like reinforcement learning, to improve the performance and interpretability of intelligent agents. Current research focuses on applying hybrid approaches to complex tasks like multi-agent navigation and multi-object manipulation, often employing architectures that integrate symbolic reasoning with neural networks. This strategy aims to overcome limitations of purely model-free or model-based methods, leading to more robust, efficient, and explainable solutions in robotics and other domains.