Optimization
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Roshan Shariff & Csaba Szepesvรกri
To achieveour result, westartfrom theapproximatelinear programming(ALP) approach where the value function is approximated using the feature vectors.
PracticalLarge-ScaleLinearProgrammingusing Primal-DualHybridGradient
We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications.
LearningtheExpectedCoreof StrictlyConvexStochasticCooperativeGames
Reward allocation, also known as the credit assignment problem, has been an important topic ineconomics, engineering, andmachine learning.
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
This bound naturally breaks into two components.
BoundaryDecompositionfor NadirObjectiveVectorEstimation
The current methods for estimating the nadir objective vector perform effectivelyonly onspecific MOPs.