mlsanityData to 14 Oct 2026

Airlift Challenge: A Competition for Optimizing Cargo Delivery

arXiv 2404.17716 ↗First posted 26 Apr 2024cs.AIcited by 1

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Abstract (arXiv)

Airlift operations require the timely distribution of various cargo, much of which is time sensitive and valuable. These operations, however, have to contend with sudden disruptions from weather and malfunctions, requiring immediate rescheduling. The Airlift Challenge competition seeks possible solutions via a simulator that provides a simplified abstraction of the airlift problem. The simulator uses an OpenAI gym interface that allows participants to create an algorithm for planning agent actions. The algorithm is scored using a remote evaluator against scenarios of ever-increasing difficulty. The second iteration of the competition was underway from November 2023 to April 2024. This paper describes the competition, simulation environment, and results. As a step towards applying generalized planning techniques to the problem, a temporal PDDL domain is presented for the Pickup and Delivery Problem, a model which lies at the core of the Airlift Challenge.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 1 of its 20 references that are in the map. First posted on arXiv 26 Apr 2024.

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References · 1

  1. 2020Flatland-RL : Multi-Agent Reinforcement Learning on Trainscited by 32

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