Target Policy

Target policy learning in reinforcement learning aims to efficiently acquire optimal policies for new tasks by leveraging knowledge from related source tasks. Current research focuses on improving transfer learning methods, such as integrating optimization and behavior transfer techniques, and developing safe evaluation frameworks to ensure reliable deployment of learned policies in real-world scenarios. These advancements are crucial for enhancing the sample efficiency and robustness of reinforcement learning algorithms, particularly in applications where data is limited or safety is paramount. This research also explores the impact of different target estimation biases, such as optimism and pessimism, on learning performance.