Cost-Aware Near-Optimal Policy Learning - He-Yueya, Joy; Lee, Jonathan; Jörke, Matthew; Brunskill, Emma (Proceedings of the AAAI Conference on Artificial Intelligence, 2025)
It is often of interest to learn a context-sensitive decision policy, such as in contextual multi-armed bandit processes. To quantify the efficiency of a machine learning algorithm for such settings, probably approximately correct (PAC) bounds, which bound the number of samples required, or cumulative regret guarantees, are typically used. However, real-world settings often have limited resources for experimentation, and decisions/interventions may differ in the amount of resources required (e.g., money or time). Therefore, it is of interest to consider how to design an experiment strategy that reduces the experimental budget needed to learn a near-optimal contextual policy. Unlike reinforcement learning or bandit approaches that embed costs into the reward function, we focus on reducing resource use in learning a near-optimal policy without resource constraints. We introduce two resource-aware algorithms for the contextual bandit setting and prove their soundness. Simulations based on real-world datasets demonstrate that our algorithms significantly reduce the resources needed to learn a near-optimal decision policy compared to previous resource-unaware methods.