Many operational Markov decision processes provide structured information where a trajectory generated under one policy may also reveal how other policies would have performed. We formalize this through an information order over a parameterized policy class, writing $π_{θ'} \preceq π_θ$ when data collected under $π_θ$ can be used to estimate the long-run average cost of $π_{θ'}$. We develop an epoch-based reinforcement learning algorithm for infinite-horizon average-cost MDPs with continuous state and action spaces that exploits this order through counterfactual inference. For a $d$-dimensional policy class whose information order has width $w$, the algorithm achieves $\widetilde O(H\sqrt{wdT/α})$ regret relative to the optimal policy in the class, where $H$ is a bias span upper bound and $α$ denotes an "information ratio". Thus, the width captures the effective exploration complexity of the problem, interpolating between full counterfactual feedback and unstructured policy learning. We instantiate the framework in lost-sales inventory systems with positive lead times, dual-sourcing systems governed by dual-index policies, and queues with state-dependent service rates. These applications yield new regret guarantees without requiring global convexity of the average-cost function or specialized arrival-rate structure. Numerical experiments show that exploiting the information order substantially improves learning performance over existing general-purpose and problem-specific benchmarks.

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