A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes to address this issue by re-using knowledge from previously learned tasks. However, determining which source task qualifies as the most appropriate for knowledge extraction, as well as the choice regarding which algorithm components to transfer, represent severe obstacles to its application in reinforcement learning. The goal of this paper is to address these issues with modular multi-source transfer learning techniques. The proposed techniques automatically learn how to extract useful information from source tasks, regardless of the difference in state-action space and reward function. We support our claims with extensive and challenging cross-domain experiments for visual control.

翻译：强化学习中的一个关键挑战是减少智能体在掌握给定任务时与环境的交互次数。迁移学习通过重用先前学习任务的知识来解决这一问题。然而，确定哪个源任务最适合用于知识提取，以及选择哪些算法组件进行迁移，是其应用于强化学习时面临的重大障碍。本文旨在利用模块化多源迁移学习技术解决这些问题。所提出的技术能够自动学习如何从源任务中提取有用信息，而无需考虑状态-动作空间和奖励函数的差异。我们通过广泛且具有挑战性的视觉控制跨领域实验来支持我们的论点。