# The Role of Coverage in Online Reinforcement Learning

@article{Xie2022TheRO, title={The Role of Coverage in Online Reinforcement Learning}, author={Tengyang Xie and Dylan J. Foster and Yu Bai and Nan Jiang and Sham M. Kakade}, journal={ArXiv}, year={2022}, volume={abs/2210.04157} }

Coverage conditions —which assert that the data logging distribution adequately covers the state space—play a fundamental role in determining the sample complexity of oﬄine reinforcement learning. While such conditions might seem irrelevant to online reinforcement learning at ﬁrst glance, we establish a new connection by showing—somewhat surprisingly—that the mere existence of a data distribution with good coverage can enable sample-eﬃcient online RL. Concretely, we show that coverability —that…

## 2 Citations

### When is Realizability Sufficient for Off-Policy Reinforcement Learning?

- Computer ScienceArXiv
- 2022

These error bounds establish that oﬀ-policy reinforcement learning remains statistically viable even in absence of Bellman completeness, and characterize the intermediate situation between the favorable Bellman complete setting and the worst-case scenario where exponential lower bounds are in force.

### Leveraging Offline Data in Online Reinforcement Learning

- Computer ScienceArXiv
- 2022

This work characterize the necessary number of online samples needed in this setting given access to some offline dataset, and develops an algorithm, FTPedel, which is provably optimal, for MDPs with linear structure.

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