Local instance-based transfer learning for reinforcement learning

Similarity-based transfer learning for reinforcement learning has garnered attention for its potential to enhance target task learning. However, it faces significant challenges in efficiency and effectiveness, primarily stemming from issues such as sparse reward, long trajectory, and strict similarity. To solve these problems, this paper proposes a local instance-based transfer learning method for reinforcement learning. Instead of relying on sparse reward and long trajectory, this approach leverages the Q value of the local trajectory to evaluate similarity, thereby significantly enhancing transfer efficiency. Furthermore, by relaxing the strictness of the similarity, three transfer policies are proposed to facilitate positive transfer. Extensive experimental results demonstrate that the effectiveness and efficiency of the proposed method in comparison with traditional similarity-based transfer learning methods.