SYLPH

Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas

Official PyTorch implementation and pretrained evaluation package for multi-agent path finding (MAPF).

Overview · Quick start · Evaluation · Training · Repository layout

Overview

SYLPH is a learning-based MAPF framework designed to reduce the homogeneous behavior caused by sharing one decentralized policy among all agents. Each agent dynamically selects a Social Value Orientation (SVO)—representing behaviors ranging from selfish to altruistic—and conditions its movement policy on that choice.

Component	Purpose
Dynamic social behavior	Lets agents choose situation-dependent SVOs to help resolve symmetric conflicts, bottlenecks, and deadlocks.
Influential-agent selection	Predicts future interactions and identifies the other agent most relevant to the current decision.
SVO-conditioned policy	Conditions movement decisions on each agent's selected social preference.
Decentralized execution	Retains the scalability of parameter sharing while allowing agents to adopt varied behaviors.

For the method and experimental results, see the paper Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas.

Quick start

1. Create the environment

Create and activate the provided Conda environment:

conda env create -f MAPF.yml
conda activate MAPF

Note

Run all commands from the repository root. Model and evaluation-data paths are relative to this directory.

2. Download the pretrained checkpoint

Download the pretrained SYLPH model before running evaluation:

python checkpoint_utils.py

The script downloads hechengyang/sylph from Hugging Face and places the checkpoint at the existing path expected by the evaluator:

models/sylph/net_checkpoint.pkl

The checkpoint is approximately 123 MiB. If the file already exists, the script reuses it instead of downloading it again.

Important

Run checkpoint_utils.py after installing the environment and before running the evaluator.

3. Run the default evaluation

python run_the_instances.py

The default evaluator runs 200 saved 32 × 32 random-map instances with 50 agents. Checkpoint inference runs on CPU, and cases are parallelized with Ray.

Pretrained evaluation

All evaluation runs expect the checkpoint downloaded by checkpoint_utils.py at models/sylph/net_checkpoint.pkl.

Choose the number of agents

Set test_num_agents near the top of run_the_instances.py:

env_length=32test_num_agents=150obs_prob_density=0.2

The selected number of agents must match one of the bundled instance sets:

Agents	Instance file
50	32length_50agents_0.2density.pth
100	32length_100agents_0.2density.pth
150	32length_150agents_0.2density.pth
200	32length_200agents_0.2density.pth
250	32length_250agents_0.2density.pth
300	32length_300agents_0.2density.pth

The instance files are stored under 32_32_0.2/.

Increase the evaluation episode limit

For evaluation, we recommend changing EnvParameters.EPISODE_LEN in alg_parameters.py from 256 to 512:

classEnvParameters:
EPISODE_LEN=512

Adjust evaluation resources

The main runtime settings are near the bottom of run_the_instances.py:

ray.init(num_cpus=20)
num_runs=200

• Lower num_cpus if fewer CPU cores are available.
• Lower num_runs for a shorter smoke test.
• Keep num_runs within the number of cases stored in the selected instance file.

After evaluation, the script reports:

• success rate — fraction of instances in which every agent reaches its goal;
• average steps — mean episode length across evaluated instances;
• reach rate — fraction of agents that reach their goals.

Training

Before starting a training run, set the following values in alg_parameters.py:

classEnvParameters:
N_AGENTS=8EPISODE_LEN=256OBSTACLE_PROB= (0, 0.4)

Important

Use these settings for training. Evaluation uses separate settings as described in the pretrained evaluation section.

After activating the environment, start training with:

python driver.py

Training parameters are defined in alg_parameters.py. Model checkpoints and animated episodes are written under models/ and gifs/ at the configured intervals.

Track training with Weights & Biases

Set RecordingParameters.WANDB = True in alg_parameters.py, then replace the placeholder account settings:

ENTITY="your_wandb_entity"EXPERIMENT_PROJECT="your_project"EXPERIMENT_NAME="your_experiment"

Configuration reference

The central configuration lives in alg_parameters.py.

Setting	Default	Description
EnvParameters.N_AGENTS	8	Number of agents used during training.
EnvParameters.EPISODE_LEN	256	Maximum training episode length; 512 is recommended for evaluation.
EnvParameters.FOV_SIZE	9	Width and height of each agent's local field of view.
EnvParameters.WORLD_SIZE	(10, 40)	Training map dimensions.
EnvParameters.OBSTACLE_PROB	(0.0, 0.3)	Training obstacle-density range.
TrainingParameters.N_ENVS	16	Number of parallel training environments.
TrainingParameters.N_MAX_STEPS	2e7	Maximum number of training steps.
SetupParameters.USE_GPU_GLOBAL	True	Runs global-model optimization on a GPU.

Repository layout

.
├── alg_parameters.py # Environment, network, and optimization settings
├── checkpoint_utils.py # Hugging Face checkpoint downloader
├── driver.py # Training entry point
├── run_the_instances.py # Parallel pretrained-model evaluation
├── mapf_gym.py # MAPF environment and execution logic
├── model.py # Model interface, inference, and optimization
├── net.py # Policy and value network
├── transformer.py # Attention modules
├── runner.py # Distributed rollout worker
├── util.py # Training, metrics, and visualization helpers
├── models/sylph/ # Pretrained checkpoint destination
└── 32_32_0.2/ # Saved evaluation instances

Troubleshooting

The pretrained checkpoint is missing

Activate the project environment and run python checkpoint_utils.py from the repository root. The downloader creates models/sylph/ automatically.

huggingface_hub is not installed

Activate the MAPF environment. If it was created before the downloader dependency was added, update it with conda env update -f MAPF.yml and run the download command again.

Ray tries to start more workers than the machine can support

Reduce num_cpus in run_the_instances.py. For a quick local check, also reduce num_runs.

The selected instance file cannot be found

Confirm that test_num_agents is one of the six supported values and that the evaluation command is being run from the repository root.

CUDA is unavailable during evaluation

The pretrained evaluator explicitly loads its checkpoint onto CPU, so a GPU is not required for run_the_instances.py.

Citation

If this repository is useful in your research, please cite:

@article{he2024social,
title={Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas},
author={He, Chengyang and Duhan, Tanishq and Tulsyan, Parth and Kim, Patrick and Sartoretti, Guillaume},
journal={arXiv preprint arXiv:2408.03063},
year={2024}
}

License

This project is released under the MIT License.