DARLING augments RL with change detection to match minimax lower bounds on dynamic regret for piecewise stationary tabular and linear MDPs under separability and reachability conditions.
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DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees
DARLING augments RL with change detection to match minimax lower bounds on dynamic regret for piecewise stationary tabular and linear MDPs under separability and reachability conditions.