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cs.LG2024
To the Max: Reinventing Reward in Reinforcement Learning
Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt
In reinforcement learning (RL), different reward functions can define the same optimal policy but result in drastically different learning performance. For some, the agent gets stu…
cs.LG2023
Learning From Scenarios for Stochastic Repairable Scheduling
Kim van den Houten, David M. J. Tax, Esteban Freydell +1
When optimizing problems with uncertain parameter values in a linear objective, decision-focused learning enables end-to-end learning of these values. We are interested in a stocha…