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Provable Benefits of Representational Transfer in Reinforcement Learning
Alekh Agarwal · Yuda Song · Kaiwen Wang · Mengdi Wang · Wen Sun · Xuezhou Zhang
Event URL: https://openreview.net/forum?id=NQrU6DLMuS »

We study the problem of representational transfer in RL, where an agent first pretrains offline in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy online in a target task. We propose a new notion of task relatedness between source and target tasks and develop a novel approach for representational transfer under this assumption. Concretely, we show that given generative access to a set of source tasks, we can discover a representation, using which subsequent linear RL techniques quickly converge to a near-optimal policy, with only online access to the target task. The sample complexity is close to knowing the ground truth features in the target task and comparable to prior representation learning results in the source tasks. We complement our positive results with lower bounds without generative access and validate our findings with empirical evaluation on rich observation MDPs that requires deep exploration.

Author Information

Alekh Agarwal (Google Research)
Yuda Song (Carnegie Mellon University)
Kaiwen Wang (Cornell University and Cornell Tech)
Mengdi Wang (Princeton University)

Mengdi Wang is interested in data-driven stochastic optimization and applications in machine and reinforcement learning. She received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013. At MIT, Mengdi was affiliated with the Laboratory for Information and Decision Systems and was advised by Dimitri P. Bertsekas. Mengdi became an assistant professor at Princeton in 2014. She received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years).

Wen Sun (Cornell University)
Xuezhou Zhang (Princeton University)

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