license: mit
task_categories:
- text-generation
language:
- en
tags:
- multi-agent-path-finding
- mapf
- planning
- llm-benchmark
pretty_name: MAPF-FrozenLake Benchmark
size_categories:
- 1K<n<10K
configs:
- config_name: benchmark_wr025
data_files:
- split: 3_agents
path: benchmark_wr025/3_agents.jsonl
- split: 4_agents
path: benchmark_wr025/4_agents.jsonl
- split: 5_agents
path: benchmark_wr025/5_agents.jsonl
- config_name: benchmark_wr050
data_files:
- split: 3_agents
path: benchmark_wr050/3_agents.jsonl
- split: 4_agents
path: benchmark_wr050/4_agents.jsonl
- split: 5_agents
path: benchmark_wr050/5_agents.jsonl
- config_name: benchmark_wr075
data_files:
- split: 3_agents
path: benchmark_wr075/3_agents.jsonl
- split: 4_agents
path: benchmark_wr075/4_agents.jsonl
- split: 5_agents
path: benchmark_wr075/5_agents.jsonl
MAPF-FrozenLake Benchmark
Evaluation benchmark for the paper From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning
Three configs (benchmark_wr025 / benchmark_wr050 / benchmark_wr075)
correspond to the wait-ratio threshold of the underlying CBS-optimal
solution (higher = more inter-agent coordination required).
Each config has three splits by agent count.
Load
from datasets import load_dataset
ds = load_dataset("LARK-Lab/MAPF-FrozenLake-Benchmark",
name="benchmark_wr075", split="5_agents")
print(ds[0]["text"][:400])
Run evaluation
Drop the downloaded folders directly into the
Trainee-to-Trainer
repo root — the directory names already match what the evaluators
expect (benchmark_wr025/ / benchmark_wr050/ / benchmark_wr075/).
Each one must contain <N>_agents/dataset_nl.jsonl.
One-shot download + layout:
hf download LARK-Lab/MAPF-FrozenLake-Benchmark \
--repo-type dataset --local-dir /tmp/mapf_bench
for wr in benchmark_wr025 benchmark_wr050 benchmark_wr075; do
for n in 3 4 5; do
mkdir -p ${wr}/${n}_agents
cp /tmp/mapf_bench/${wr}/${n}_agents.jsonl \
${wr}/${n}_agents/dataset_nl.jsonl
done
done
Then run the evaluators shipped with the code repo:
# HuggingFace-format model
DATA_ROOT=benchmark_wr075 sbatch test_model_hf.sh \
/path/to/model "3,4,5" "3,4,5,6,7,8,9,10" my_tag
# FSDP RL checkpoint
DATA_ROOT=benchmark_wr075 sbatch test_model_rl.sh \
/path/to/outputs/.../global_step_XXX
# OpenAI-compatible API model
bash test_model_api.sh <endpoint> <model-id> <api-key>
Each run prints per-(agent-count, map-size) optimal-rate and accuracy at the end of its log.
License
MIT.