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Hindsight Learning for MDPs with Exogenous Inputs. (arXiv:2207.06272v1 [cs.LG])
July 14, 2022, 1:10 a.m. | Sean R. Sinclair, Felipe Frujeri, Ching-An Cheng, Adith Swaminathan
cs.LG updates on arXiv.org arxiv.org
We develop a reinforcement learning (RL) framework for applications that deal
with sequential decisions and exogenous uncertainty, such as resource
allocation and inventory management. In these applications, the uncertainty is
only due to exogenous variables like future demands. A popular approach is to
predict the exogenous variables using historical data and then plan with the
predictions. However, this indirect approach requires high-fidelity modeling of
the exogenous process to guarantee good downstream decision-making, which can
be impractical when the exogenous process …
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