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arXiv · 2009.10396

Is Q-Learning Provably Efficient? An Extended Analysis

Kushagra Rastogi·Jonathan Lee·Fabrice Harel-Canada·Aditya Joglekar
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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.

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Kushagra Rastogi, Jonathan Lee, Fabrice Harel-Canada, Aditya Joglekar. 2020-09-22. Is Q-Learning Provably Efficient? An Extended Analysis. https://arxiv.org/abs/2009.10396

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