Towards Reinforcement Learning Representation Transfer

Transfer learning problems are typically framed as leveraging knowledge learned on a source task to improve learning on a related, but different, target task. Current transfer methods are able to successfully transfer knowledge between agents in different reinforcement learning tasks, reducing the time needed to learn the target. However, the complimentary task of representation transfer, i. e. transferring knowledge between agents with different internal representations, has not been well explored. The goal in both types of transfer problems is the same: reduce the time needed to learn the target with transfer, relative to learning the target without transfer. This work introduces one such representation transfer algorithm which is implemented in a complex multiagent domain. Experiments demonstrate that transferring the learned knowledge between different representations is both possible and beneficial.

Authors

• Matthew E. Taylor University of Alberta Alberta Machine Intelligence Institute (Amii)
• Peter Stone University of Texas at Austin & Sony AI

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