Airlifts demand the delivery of large sets of cargo into areas of need under tight deadlines. Yet, there are many obstacles preventing timely delivery. Airports have limited capacity to process airplanes, thus limiting throughput and potentially creating bottlenecks. Weather disruptions can cause delays or force airplanes to re-route. Unexpected cargo may be staged for an urgent delivery.
This competition challenges participants to design agents that can plan and execute an airlift operation. Quick decision-making is needed to rapidly adjust plans in the face of disruptions along the delivery routes. The decision-maker will also need to incorporate new cargo delivery requests that appear during the episode. The primary objective is to meet the specified deadlines, with a secondary goal of minimizing cost. Solutions can incorporate machine learning, optimization, path planning heuristics, or any other technique.
This page summarizes key aspects of the competition. To view more details, select a section from the menu on the left.
The air network consists of a graph, where nodes are capacity-constrained airports, and edges are routes with an associated cost and time-of-flight. Each cargo item is stored at a node, and must be picked up by agents (airplanes) and delivered to a destination node. Different airplane models can have different route networks. In fact, the network for a specific model may be disconnected, meaning that some airplanes may not be able to reach all airports. Time is needed after an airplane lands to refuel and to load/unload cargo, taking up precious processing capacity at the airport. There are two delivery deadlines: a soft deadline by which the cargo is desired, and a hard deadline after which the delivery is considered missed (with a heavy penalty).
A small example scenario is shown below. Airports (small squares) are shown with connecting routes (white lines). Cargo is staged at three airports in the pickup area (green rectangle). Each is designated for delivery at a specific airport in the area of need (yellow circle). The agent algorithm guides four airplanes through the network to pick up and deliver the cargo. Routes undergo random disruptions, requiring airplanes to either wait for the disruption to clear, or follow a different route.
For more details, see the Model documentation.
The simulation environment is written in Python and follows the PettingZoo multi-agent reinforcement learning interface. An agent issues an action for each airplane, indicating which cargo to load/unload at an airport and which airport to fly to next. The agents observe a number of state variables, including airplane status, cargo locations, route availability, etc… A NetworkX object is provided for each airplane, allowing the agent to easily plan paths using existing library methods. A reward signal generated by the environment penalizes late deliveries, missed deliveries, and movement.
A minimal agent code example follows.
from airlift.envs import AirliftEnv, AirliftWorldGenerator, ActionHelper # Agent algorithm goes here def policy(obs): actions = ActionHelper.sample_valid_actions(obs) return actions env = AirliftEnv(AirliftWorldGenerator()) obs = env.reset() while True: actions = policy(obs) obs, rewards, dones, infos = env.step(actions) env.render() if all(dones.values()): break
For more details, see the Interface documentation.
Scoring and Evaluation#
Each episode is assigned a score based on missed deliveries, late deliveries, and total flight cost. This score is normalized against baseline algorithms: participants will receive a score of 0 if they only perform as well as a random agent, and will receive a score of 1 if they perform as well as a simple “shortest path” baseline algorithm. Scores greater than 1 indicate that the algorithm is exceeding the performance of the baselines.
An algorithm will be evaluated over a number of episode scenarios. Scenarios are generated according to a random generative model, with scenarios becoming progressively more difficult. In the beginning stages, there will be one type of airplane which can reach all airports in the air network. Later stages will have specialized airplane types: large aircraft can carry large cargo loads over long distances, but cannot land at small airports located in the drop off area. Instead, they will need to leave cargo at intermediate airports where light aircraft can retrieve the cargo and complete the delivery.
The evaluation will proceed until either:
the percentage of missed deliveries exceeds a preset threshold, or
a time limit is reached.
The overall score will be the sum of the normalized scores over all episodes. In addition to performing well on individual episodes, algorithms can also increase their score by completing more episodes.
For more details, see the Evaluation documentation.
Warm Up Phase: October 30th, 2023.
Competition Phase Begins: November 22nd, 2023
Competition Phase Ends: February 19th, 2024
Results Annoucement: March 1st, 2024
Students are encouraged to participate in this challenge. Top student submissions will be recognized and receive award certificates. If you are a student please utilize your university e-mail when registering at CodaLab.
Links to the CodaLab website & test scenarios for the ICAPS 2024 competition will be provided to here.
Previous Resources from SPIE 2023 Competition#
Feel free to look over the previous CodaLab competition website and Test Scenarios. It is important to note that here will be several changes to the environment since the previous competition.
CodaLab: Competition Platform
CodaLab Forums: Primary means of communicating with the development team
Simple Scenarios: Used for debugging your solution
Round 1 Test Scenarios: Test scenarios used for the Competition Phase
Starter kit: Everything you need to get started for writing and submitting a solution
Simulator source code: Contains the entire environment source code
Carmen Chiu, Adis Delanovic, Jill Platts, Alexa Loy, and Andre Beckus. A methodology for flattening the command and control problem space. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV. International Society for Optics and Photonics, SPIE, 2022. URL: https://doi.org/10.1117/12.2615180.
Steven F. Baker, David P. Morton, Richard E. Rosenthal, and Laura Melody Williams. Optimizing military airlift. Operations Research, 50(4):582–602, 2002. URL: https://doi.org/10.1287/opre.50.4.582.2864.
Dimitris Bertsimas, Allison Chang, Velibor V. Mišić, and Nishanth Mundru. The airlift planning problem. Transportation Science, 53(3):773–795, 2019. URL: https://doi.org/10.1287/trsc.2018.0847, doi:10.1287/trsc.2018.0847.
Gerald G. Brown, W. Matthew Carlyle, Robert F. Dell, and John W. Brau. Optimizing intratheater military airlift in iraq and afghanistan. 2013. Military Operations Research, 18(3), pp. 35-52. URL: http://hdl.handle.net/10945/38129.