Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding
Authors: Zhe Chen, Daniel Harabor, Jiaoyang Li, Peter J. Stuckey
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the idea in two large-scale settings: oneshot MAPF, where each agent has a single destination, and lifelong MAPF, where agents are continuously assigned new destinations. Empirically, we report large improvements in solution quality for one-short MAPF and in overall throughput for lifelong MAPF. |
| Researcher Affiliation | Academia | 1Department of Data Science and Artificial Intelligence, Monash University, Melbourne, Australia 2Robotics Institute, Carnegie Mellon University, Pittsburgh, USA 3OPTIMA Australian Research Council ITTC, Melbourne, Australia |
| Pseudocode | Yes | Algorithm 1: PIBT. In each iteration Plan Step computes a next move θ for each agent a A, currently at position ϕ, using a priority ordering p. |
| Open Source Code | Yes | Implementations1 are written in C++... 1https://github.com/nobodyczcz/Guided-PIBT |
| Open Datasets | No | Our maps are: Warehouse: a 500 140 synthetic fulfillment center map... Sortation: a 33 57 synthetic sortation centre map... Game: ost003d, a 194 194 map... Room: room-64-64-8, a 64 64 synthetic map... (No direct link, DOI, or explicit citation provided for these map sources as public datasets.) |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits for reproduction. |
| Hardware Specification | Yes | Implementations1 are written in C++ and evaluated on a Nectar Cloud VM instance with 32 AMD EPYC-Rome CPUs and 64 GB RAM. |
| Software Dependencies | No | Implementations1 are written in C++ and evaluated on a Nectar Cloud VM instance with 32 AMD EPYC-Rome CPUs and 64 GB RAM. (Only the programming language is mentioned, not specific libraries with version numbers.) |
| Experiment Setup | Yes | For Lifelong MAPF, the maximum simulation time is based on the size of the map: we compute a maximum number of timesteps as (width + height) * 5 with the intention that each agent has the opportunity to complete approximately 5 tasks. In lifelong MAPF experiments, planners have 10 seconds to return actions for all agents at every timestep. In one-shot MAPF experiments, planners have 60 seconds timelimit. |