A data scientist shares a two-year project building a hybrid MARL-LP (Multi-Agent Reinforcement Learning + Linear Programming) system for logistics scheduling. The post explains why standard solvers, genetic algorithms, and pure RL approaches were insufficient, then describes a MARL architecture where decentralized hub agents
Table of contents
IntroductionBusiness ContextBig Picture ProblemSystem SpecificationsWhy Not Standard Solvers?Linear OptimizationGenetic AlgorithmsWhy not Pure RL?Implemented SolutionA Glimpse of the PerformanceConstraints and BenefitsSort: