This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient? by Jin et al. We include a survey of related research to contextualize the need for strengthening the theoretical guarantees related to perhaps the most important threads of model-free reinforcement learning. We also expound upon the reasoning used in the proofs to highlight the critical steps leading to the main result showing that Q-learning with UCB exploration achieves a sample efficiency that matches the optimal regret that can be achieved by any model-based approach.
@article{arxiv.2009.10396,
title = {Is Q-Learning Provably Efficient? An Extended Analysis},
author = {Kushagra Rastogi and Jonathan Lee and Fabrice Harel-Canada and Aditya Joglekar},
journal= {arXiv preprint arXiv:2009.10396},
year = {2020}
}