Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
This is a PyTorch implementation of the paper: Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
Project Website: https://sites.google.com/view/ace-aamas
You could start training with by running sh train_gridworld.sh in directory onpolicy/scripts.
Similar to training, you could run sh render_gridworld.sh in directory onpolicy/scripts to start evaluation. Remember to set up your path to the cooresponding model, correct hyperparameters and related evaluation parameters.
We also provide our implementations of planning-based baselines. You could run sh render_gridworld_ft.sh to evaluate the planning-based methods. Note that algorithm_name determines the method to make global planning. It can be set to one of mappo, ft_rrt, ft_apf, ft_nearest and ft_utility.
You could also visualize the result and generate gifs by adding --use_render and --save_gifs to the scripts.
If you find this repository useful, please cite our paper:
@misc{yu2023asynchronous,
title={Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration},
author={Chao Yu and Xinyi Yang and Jiaxuan Gao and Jiayu Chen and Yunfei Li and Jijia Liu and Yunfei Xiang and Ruixin Huang and Huazhong Yang and Yi Wu and Yu Wang},
year={2023},
eprint={2301.03398},
archivePrefix={arXiv},
primaryClass={cs.RO}
}