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.
翻译:近年来,在编程机器人任务中采用了基于学习的方法,然而,通常不仅在初始任务学习方面,而且在将学习到的模型推广到同一任务中,而且在不同的环境中都需要广泛的培训。在本文件中,我们提出了一个新的深强化学习算法,以便在机器人任务学习问题中高效的任务概括和环境适应性。拟议方法能够有效地通过模型集成对以前学到的任务加以归纳,以解决环境适应问题。拟议的深模型集成方法(DMF)重新使用,并结合了以前训练过的模型,以提高学习效率和成果。此外,我们还采用了一种多目标向导(MGR)塑造技术,以进一步提高培训效率。拟议方法以各种环境中的以往方法为基准,以确认其有效性。