Research map / Multi-agent systems and games / Human-AI and goal-conditioned
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LaCAM instead of baseline sub-optimal MAPF solvers | MAPF benchmark scenarios · MAPF benchmark | success rate | Helped |
| Use LaCAM instead of PIBT | MAPF benchmark scenarios · MAPF benchmark | success rate | Helped |
| Use LaCAM instead of PIBT+ | MAPF benchmark scenarios · MAPF benchmark | SOC | Helped |
| Use LaCAM instead of PP, EECBS, or LNS2 | small complicated MAPF instances · small complicated instances | SOC | Hurt |
| Use LaCAM instead of baseline methods | 400-agent instances · random-32-32-20 | success rate | Helped |
| Use LaCAM rather than the tested competing solvers | massive-agent MAPF · warehouse-20-40-10-2-2, up to 10,000 agents | runtime | Helped |
| Add the high-level reinsert operation (ablation) | LaCAM · random-32-32-20 | SOC | Helped |
| Replace PIBT configuration generation with greedy single-step prioritized planning (ablation) | LaCAM · random-32-32-20 | success rate | Hurt |
| Increase the number of agents in the narrow-corridor adversarial instance | LaCAM · adversarial instance | high-level search iterations: 128; 23,907; 287,440 | Hurt |
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