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cs.LG2025
Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization
Paul Strang, Zacharie Alès, Côme Bissuel +3
Mixed-Integer Linear Programming (MILP) lies at the core of many real-world combinatorial optimization (CO) problems, traditionally solved by branch-and-bound (B&B). A key driver i…
cs.LG2022
Neural Distillation as a State Representation Bottleneck in Reinforcement Learning
Valentin Guillet, Dennis G. Wilson, Carlos Aguilar-Melchor +1
Learning a good state representation is a critical skill when dealing with multiple tasks in Reinforcement Learning as it allows for transfer and better generalization between task…