Is Q-Learning Provably Efficient? An Extended Analysis
It provides stronger theoretical guarantees for model-free reinforcement learning, which is incremental as it builds on prior work.
This paper extends the analysis of Q-learning's theoretical efficiency, showing that Q-learning with UCB exploration achieves sample efficiency matching the optimal regret of model-based approaches.
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.