Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
The episode discusses the paper establishing near-optimal sample complexity bounds for Constrained Average-Reward MDPs. Researchers provide tight upper and lower bounds for learning a policy that maximizes long-run reward while respecting a safety constraint, proving that the strict feasibility case is fundamentally harder.