Academic Background Deep Reinforcement Learning (DRL) has achieved remarkable success in various fields such as gaming, robotics, navigation, computer vision, and finance. However, existing DRL algorithms generally suffer from low sample efficiency, requiring vast amounts of data and training steps to achieve desired performance. Particularly in co...