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Policy Learning for Balancing Short-Term and Long-Term Rewards
May 7, 2024, 4:42 a.m. | Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng
cs.LG updates on arXiv.org arxiv.org
Abstract: Empirical researchers and decision-makers spanning various domains frequently seek profound insights into the long-term impacts of interventions. While the significance of long-term outcomes is undeniable, an overemphasis on them may inadvertently overshadow short-term gains. Motivated by this, this paper formalizes a new framework for learning the optimal policy that effectively balances both long-term and short-term rewards, where some long-term outcomes are allowed to be missing. In particular, we first present the identifiability of both rewards …
abstract arxiv cs.lg decision domains framework impacts insights long-term makers paper policy researchers seek significance stat.ml them type while
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