1 citations · 1 across the 2 of their papers we have counts for
2 papers
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★ 1 cited
You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization
Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt
Predict and optimize is an increasingly popular decision-making paradigm that employs machine learning to predict unknown parameters of optimization problems. Instead of minimizing…