<ul data-eligibleForWebStory="false"><li>Reinforcement Learning (RL) is a machine learning approach where an 'agent' learns to make decisions to maximize rewards in a given 'environment.' It is well-suited for the complexities of last-mile delivery.</li><li>RL models for last-mile delivery require rich real-world data to optimize route selection and re-routing decisions, considering factors like traffic and weather.</li><li>An example of RL application is the RL4CO Multi-Trip Vehicle Routing Problem solution, which efficiently optimizes complex routes, resulting in reduced costs, faster delivery times, and improved customer experience.</li><li>Despite challenges, advancements in computing power and data technologies are making RL implementation in last-mile delivery a reality, promising significant improvements in efficiency and opening doors to autonomous delivery vehicle innovations.</li></ul>

Revolutionizing Last-Mile Delivery: Reinforcement Learning for Hyper-Efficient Supply Chains

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