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About this paper
Matthew T. Regehr, Alex Ayoub
Regehr and Ayoub from the University of Alberta present an 'elementary proof' for the almost sure convergence of Q-learning by introducing an 'action-replay process' framework. Their work provides a self-contained derivation requiring minimal external mathematical results, making Q-learning's fundamental theoretical guarantee more accessible to a broader audience.
Key insights
- The 'action-replay process' framework transforms the Q-learning convergence problem into analyzing an auxiliary Markov Decision Process.
- This approach allows connecting Q-learning iterates directly to the optimal value function of the auxiliary process, simplifying the convergence analysis.
- The proof formally reinforces the necessity of the Robbins-Monro conditions: infinite state-action visitation and decaying learning rates, which are crucial for Q-learning convergence.
Summary by alphaXiv.
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