Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set
This addresses the long-standing divergence issue in reinforcement learning for practitioners, providing theoretical guarantees for a fundamental algorithm, though it is incremental as it extends prior boundedness results to convergence rates.
The paper establishes the first L^2 convergence rate for linear Q-learning iterates to a bounded set, building on prior work that proved almost sure boundedness, without requiring modifications to the algorithm, Bellman completeness, or near-optimal behavior policies, using only an ε-softmax policy with adaptive temperature.
$Q$-learning is one of the most fundamental reinforcement learning algorithms. It is widely believed that $Q$-learning with linear function approximation (i.e., linear $Q$-learning) suffers from possible divergence until the recent work Meyn (2024) which establishes the ultimate almost sure boundedness of the iterates of linear $Q$-learning. Building on this success, this paper further establishes the first $L^2$ convergence rate of linear $Q$-learning iterates (to a bounded set). Similar to Meyn (2024), we do not make any modification to the original linear $Q$-learning algorithm, do not make any Bellman completeness assumption, and do not make any near-optimality assumption on the behavior policy. All we need is an $ε$-softmax behavior policy with an adaptive temperature. The key to our analysis is the general result of stochastic approximations under Markovian noise with fast-changing transition functions. As a side product, we also use this general result to establish the $L^2$ convergence rate of tabular $Q$-learning with an $ε$-softmax behavior policy, for which we rely on a novel pseudo-contraction property of the weighted Bellman optimality operator.