The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlying Markov chain is mixing.
Whereas existing work typically assumes a contraction property, e.g., through environment mixing, we consider the setting where we are given a reward-agnostic $m$-step mixing policy, and we control the contraction and exploration properties of the learned policy by regularizing toward the mixing policy.
We provide finite-time analysis of $Q$-learning algorithms that can be instantiated with multiple choices of regularization, with guarantees measured with respect to the regularized fixed point.
Our framework exposes novel regularizer-dependent objects that control contraction, exploration, and operator bias.
In the one-step setting, our rates match the best known rates for $Q$-iteration and synchronous $Q$-learning, and for $m$-step mixing we derive new rates.
We also study $Q$-planning variants, and show that planning acts as time-localized learning, where the planning horizon $H$ trades off the bias from the boundary value against the sample complexity.
Finally, we present numerical studies validating the analysis:
- knowledge of a strong mixing policy improves convergence and exploration, and its advantage increases with bottleneck severity
- our theory predicts the convergence and bias rankings of regularizers from the $α$-divergence family