NeurIPS 2020
### Provably adaptive reinforcement learning in metric spaces

### Meta Review

This paper is about model-free RL where the state-action state is a metric space. An improved analysis of an existing algorithm (with some modifications) is shown to achieve a regret that scales with the zooming dimension of the metric space, instead of the covering dimesion. A general consensus among reviewers emerged that this theoretical RL paper is well executed, and provides a reasonable though not groundbreaking contribution to the RL literature.