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Multi-Agent Path Finding research benchmarks – Page 3 · SOTA2 Research

Multi-Agent Path Finding

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

MAPF 4x4 grid, n=3, 9 obstacles

0Collision Rate

5

Apr 10, 2026

warehouse 38 robots (complete instances)

0Node Count

3

Jul 2, 2026

room 20 robots (complete instances)

0# Nodes

3

Jul 2, 2026

random 41 robots complete instances

0Nodes Expanded

3

Jul 2, 2026

51 robots (complete instances)

0Nodes (%)

3

Jul 2, 2026

MAPF 3x3 grid, n=3, 3 obstacles

0Collision Rate

3

Apr 10, 2026

10x10 Multi 1.0 (test)

Classical (A* with prioritized planning)

0.003Time (s)

2

Feb 26, 2026

5x5 Multi 1.0 (test)

Classical (A* with prioritized planning)

0.001Time (s)

2

Feb 26, 2026

5x5 Single 1.0 (test)

Classical (A* with prioritized planning)

9Path Length (steps)

2

Feb 26, 2026

CL-CBS obstacles 20 agents

4.06Speedup Factor

1

Feb 26, 2026

CL-CBS hard 30 agents

3.19Speedup

1

Feb 26, 2026

CL-CBS easy 10 agents

2.23Speedup Factor

1

Feb 26, 2026

—

—Primary metric

0

Feb 26, 2026

Grid-based MAPF

—

—Primary metric

0

Feb 26, 2026

Page 3 of 3

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Setup MAPF-FrozenLake 3-agent Random Map CL-CBS

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