Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Multi-Agent Path Finding with Delay Probabilities
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AME with MCPs rather than Push and Swap | MAPF-DP solving · random 1, 35 agents | average makespan: 812.41 $\pm$ 0.40 -> 71.28 $\pm$ 0.34 | Helped |
| Use AME with MCPs rather than Push and Swap | MAPF-DP solving · warehouse 1, 35 agents | average makespan: 1,399.14 $\pm$ 0.43 -> 124.18 $\pm$ 0.44 | Helped |
| Replace MCPs with Fully Synchronized Policies (ablation) | AME · random 1 | average makespan: 71.28 $\pm$ 0.34 -> 140.29 $\pm$ 0.50 | Hurt |
| Replace MCPs with Fully Synchronized Policies (ablation) | AME · warehouse 1 | average makespan: 124.18 $\pm$ 0.44 -> 219.63 $\pm$ 0.65 | Hurt |
| Use a non-robust policy that always issues GO commands instead of MCPs (ablation) | AME · random 1 | average makespan: 71.28 $\pm$ 0.34 -> 67.82 $\pm$ 0.35 | No clear effect |
| Use a non-robust policy that always issues GO commands instead of MCPs (ablation) | AME · warehouse 1 | average makespan: 124.18 $\pm$ 0.44 -> 122.42 $\pm$ 0.42 | No clear effect |
| Increase the number of agents | AME on 30x30 grids · 50 to 200 agents | solved (%): 0.94; 0.68; 0.10; 0 | Hurt |
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