• Corpus ID: 244477645

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation

  title={Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation},
  author={Dylan J. Foster and Akshay Krishnamurthy and David Simchi-Levi and Yunzong Xu},
  booktitle={Annual Conference Computational Learning Theory},
We consider the offline reinforcement learning problem, where the aim is to learn a decision making policy from logged data. Offline RL—particularly when coupled with (value) function approximation to allow for generalization in large or continuous state spaces—is becoming increasingly relevant in practice, because it avoids costly and time-consuming online data collection and is well suited to safety-critical domains. Existing sample complexity guarantees for offline value function… 

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