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Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of practical application. However, extrapolation error generated by distribut
Externí odkaz:
http://arxiv.org/abs/2301.01298
Publikováno v:
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:323-339, 2023
Reinforcement learning (RL) is an effective framework for solving sequential decision-making tasks. However, applying RL methods in medical care settings is challenging in part due to heterogeneity in treatment response among patients. Some patients
Externí odkaz:
http://arxiv.org/abs/2110.02879