Is Q-Learning Provably Efficient? An Extended Analysis

TL;DR

This paper extends the theoretical analysis of Q-learning with UCB exploration, demonstrating it achieves sample efficiency comparable to optimal model-based methods, and provides a survey of related research.

Contribution

It offers a detailed proof analysis showing Q-learning's provable efficiency and contextualizes it within existing research on reinforcement learning guarantees.

Findings

Q-learning with UCB exploration matches optimal regret bounds.

Provides a comprehensive survey of related reinforcement learning research.

Highlights critical proof steps for Q-learning's efficiency.

Abstract

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.