  Learning-based methods have been used to pro-gram robotic tasks in recent
years. However, extensive training is usually required not only for the initial
task learning but also for generalizing the learned model to the same task but
in different environments. In this paper, we propose a novel Deep Reinforcement
Learning algorithm for efficient task generalization and environment adaptation
in the robotic task learning problem. The proposed method is able to
efficiently generalize the previously learned task by model fusion to solve the
environment adaptation problem. The proposed Deep Model Fusion (DMF) method
reuses and combines the previously trained model to improve the learning
efficiency and results.Besides, we also introduce a Multi-objective Guided
Reward(MGR) shaping technique to further improve training efficiency.The
proposed method was benchmarked with previous methods in various environments
to validate its effectiveness.
