Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding
Authors: Keisuke Okumura
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Exhaustive experiments demonstrate their utility. For instance, La CAM sub-optimally solved 99% of the instances retrieved from the MAPF benchmark, where the number of agents varied up to a thousand, within ten seconds on a standard desktop PC, while ensuring eventual convergence to optima; developing a new horizon of MAPF algorithms. |
| Researcher Affiliation | Academia | Keisuke Okumura1,2 1National Institute of Advanced Industrial Science and Technology (AIST) 2University of Cambridge |
| Pseudocode | Yes | Algorithm 1 La CAM. ... Algorithm 2 PIBT ... Algorithm 3 La CAM ... Algorithm 4 procedure PIBT with swap |
| Open Source Code | Yes | The appendix and code are available at https://kei18.github.io/lacam2/. |
| Open Datasets | Yes | All experiments used four-connected grid maps retrieved from the MAPF benchmark [Stern et al., 2019]. |
| Dataset Splits | No | The paper mentions using instances from the MAPF benchmark and increasing the number of agents, but it does not specify explicit training, validation, or testing splits for the data used in the experiments. It evaluates the algorithm directly on problem instances. |
| Hardware Specification | Yes | The experiments were run on a desktop PC with Intel Core i7-7820X 3.6 GHz CPU and 32 GB RAM. |
| Software Dependencies | No | La CAM was coded in C++. This only specifies the programming language, not any specific software dependencies with version numbers. |
| Experiment Setup | Yes | A maximum of 16 different instances were run in parallel using multi-threading. ... Unless mentioned, this section uses a timeout of 30 s for solving MAPF. ... with a small probability (e.g., 0.1%), the implementation reinserts a node of S instead of the found one. |