DPO

Note

The implementation of the following methods can be found in this codebase:

• DPO: A Fully Decentralized Surrogate for Multi-Agent Policy Optimization

Installation

• 1. install SMAC following https://github.com/oxwhirl/smac
• 2. install Multi-Agent MuJoCo following https://github.com/schroederdewitt/multiagent_mujoco
• 3. install MPE following https://github.com/openai/multiagent-particle-envs
• 4. install required packages: pip install -r requirements.txt

How to run

 python3 on-policy-main/train_smac.py  --map_name 2s3z --use_eval  --penalty_method True --dtar_kl 0.02   --experiment_name dtar_0.02_V_penalty_2M --num_env_steps 2000000 --group_name dpo --seed 1 --multi_rollout True --n_rollout_threads 1

Results

Here, we provide results in three different SMAC scenarios using default hyperparameters. --->

Citation

If you are using the codes, please cite our papers.

Kefan Su and Zongqing Lu. A Fully Decentralized Surrogate for Multi-Agent Policy Optimization. TMLR, 2024

@article{DPO,
title={A Fully Decentralized Surrogate for Multi-Agent Policy Optimization},
author={Su, Kefan and Lu, Zongqing},
journal={Transactions on Machine Learning Research},
year={2024}
}