Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning

Understand

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.