Trading off rewards and errors in multi-armed bandits
The research formalizes balancing maximizing cumulative rewards and minimizing estimation errors in multi-armed bandit problems. It introduces the ForcingBalance algorithm, which uses a weighted objective function to find a sequence of pulls that optimizes this trade-off. The algorithm achieves performance matching the best possible rates for both reward maximization and active exploration.