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This work shows that for every fixed rational discount factor ($\gamma\in(0,1)$), exact planning remains NP-hard, and introduces a randomized polynomial-time approximation scheme for every fixed look-ahead depth.
Balancing automation and human intervention can significantly enhance efficiency in service systems, with the UCB-DPP policy proving to be a game-changer in managing this tradeoff.
Achieving optimal competitive ratios in prophet inequalities without relying on offline samples could revolutionize online learning strategies.
Even slight context changes in matching markets can drastically alter optimal matchings and tank player utility, but this new algorithm dynamically adapts to minimize regret in both stochastic and adversarial environments.
Even when shill bids don't affect who wins, they can still dramatically alter the statistical difficulty of learning to bid in auctions.
