Transfer Learning Via Linear Multi-Variable Mapping under Reinforcement Learning Framework

Though popular in many agent learning tasks, reinforcement learning still faces problems, such as long learning time in complex environment. Transfer learning could shorten the learning time and improve the performance in reinforcement learning by reusing the knowledge acquired from different but related source task. Due to the difference in state space and/or action space of the target and source task, transfer via inter-task mapping is a popular method. The design of the inter-task mapping is very critical to this transfer learning method. In this paper, we propose a linear multi-variable mapping (LMVM) for the transfer learning to make a better use of the knowledge learned from the source task. Unlike the inter-task mapping used before, the LMVM is not a one-to-one mapping but a one-to-many mapping, which is based on the idea that the element in target task is related with several similar elements from source task. We test transfer learning via our new mapping on the Keepaway platform. The experimental results show that our method could make the reinforcement learning agents learn much faster than those without transfer and those transfer with simpler mappings.