Awesome

Multi-Agent Constrained Policy Optimisation (MACPO)

The repository is for the paper: Multi-Agent Constrained Policy Optimisation, in which we investigate the problem of safe MARL. The problem of safe multi-agent learning with safety constraints has not been rigorously studied; very few solutions have been proposed, nor a sharable testing environment or benchmarks. To fill these gaps, in this work, we formulate the safe multi-agent reinforcement learning problem as a constrained Markov game and solve it with trust region methods. Our solutions---Multi-Agent Constrained Policy Optimisation (MACPO) and MAPPO-Lagrangian---leverage on the theory of Constrained Policy Optimisation (CPO) and multi-agent trust region learning, and critically, they enjoy theoretical guarantees of both monotonic improvement in reward and satisfaction of safety constraints at every iteration. Experimental results reveal that MACPO/MAPPO-Lagrangian significantly outperform baselines in terms of balancing the performance and constraint satisfaction, e.g. MAPPO, IPPO, HAPPO.

Environments Supported:

Safety Multi-Agent Mujoco

1. Installation

1.1 Create Environment

# create conda environment
conda create -n macpo python==3.7
conda activate macpo
pip install -r requirements.txt
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

cd MACPO/macpo (for the macpo algorithm) or cd MAPPO-Lagrangian/mappo_lagrangian (for the mappo_lagrangian algorithm)
pip install -e .

1.2 Install Safety Multi-Agent Mujoco

Install mujoco accoring to mujoco-py and MuJoCo website.
clone Safety Multi-Agent Mujoco to the env path (in this repository, have set the path).

LD_LIBRARY_PATH=${HOME}/.mujoco/mujoco200/bin;
LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

2. Train

cd MACPO/macpo/scripts or cd MAPPO-Lagrangian/mappo_lagrangian/scripts
chmod +x ./train_mujoco.sh
./train_mujoco.sh

3. Results

<div align=center> <img src="https://github.com/chauncygu/Multi-Agent-Constrained-Policy-Optimisation/blob/main/figures/wall1_manyagent_ant.png" width="850"/> <img src="https://github.com/chauncygu/Multi-Agent-Constrained-Policy-Optimisation/blob/main/figures/New_Ant_results.png" width="850"/> <img src="https://github.com/chauncygu/Multi-Agent-Constrained-Policy-Optimisation/blob/main/figures/New_HalfCheetah_results.png" width="850"/> </div> <div align=center> <center style="color:#000000;text-decoration:underline"> Performance comparisons on tasks of Safe ManyAgent Ant, Safe Ant, and Safe HalfCheetah in terms of cost (the first row) and reward (the second row). The safety constraint values are: 1 for ManyAgent Ant, 0.2 for Ant, and 5 for HalfCheetah. Our methods consistently achieve almost zero costs, thus satisfying safe constraints, on all tasks. In terms of reward, our methods outperform <a href="https://arxiv.org/abs/2011.09533)">IPPO</a> and <a href="https://arxiv.org/abs/2103.01955">MAPPO</a> on some tasks but underperform <a href="https://arxiv.org/abs/2109.11251">HAPPO</a>, which is also an unsafe algorithm.</center> </div>

4. Demos

Ant Task: the width of the corridor set by two walls is 10 m. The environment emits the cost of 1 for an agent, if the distance between the robot and the wall is less than 1.8 m, or when the robot topples over.


A demo denotes <b>unsafe</b> performance using <a href="https://arxiv.org/abs/2109.11251">HAPPO</a> on Ant-2x4 task.</center>	A demo denotes <b>safe</b> performance using <a href="http://arxiv.org/abs/2110.02793">MAPPO-Lagrangian</a> on Ant-2x4 task.</center>

HalfCheetah Task: In the task, the agents move inside a corridor (which constraints their movement, but does not induce costs). Together with them, there are bombs moving inside the corridor. If an agent finds itself too close to the bomb, the distance between an agent and the bomb is less than 9m, a cost of 1 will be emitted, at the same time, the bomb will turn blood red.


A demo denotes <b>unsafe</b> performance using <a href="https://arxiv.org/abs/2109.11251">HAPPO</a> on HalfCheetah-2x3 task.</center>	A demo denotes <b>safe</b> performance using <a href="http://arxiv.org/abs/2110.02793">MAPPO-Lagrangian</a> on HalfCheetah-2x3 task.</center>

ManyAgent Ant Task One: In the ManyAgent Ant task, the width of the corridor set by two walls is 9m. The environment emits the cost of 1 for an agent, if the distance between the robot and the wall is less than 1.8 m, or when the robot topples over.


A demo denotes <b>unsafe</b> performance using <a href="https://arxiv.org/abs/2109.11251">HAPPO</a> on ManyAgent Ant-2x3 task.</center>	A demo denotes <b>safe</b> performance using <a href="http://arxiv.org/abs/2110.02793">MAPPO-Lagrangian</a> on ManyAgent Ant-2x3 task.</center>

ManyAgent Ant Task Two: In the ManyAgent Ant task, the width of the corridor is 12 m; its walls fold at the angle of 30 degrees. The environment emits the cost of 1 for an agent, if the distance between the robot and the wall is less than 1.8 m, or when the robot topples over.


A demo denotes <b>unsafe</b> performance using <a href="https://arxiv.org/abs/2109.11251">HAPPO</a> on ManyAgent Ant-2x3 task.</center>	A demo denotes <b>unsafe</b> performance using <a href="http://arxiv.org/abs/2110.02793">MAPPO-Lagrangian</a> on ManyAgent Ant-2x3 task.</center>	A demo denotes <b>safe</b> performance using <a href="http://arxiv.org/abs/2110.02793">MACPO</a> on ManyAgent Ant-2x3 task.</center>

5. Publication

If you find the repository useful, please cite the paper:

@article{gu2023safe,
  title={Safe Multi-Agent Reinforcement Learning for Multi-Robot Control},
  author={Gu, Shangding and Kuba, Jakub Grudzien and Chen, Yuanpei and Du, Yali and Yang, Long and Knoll, Alois and Yang, Yaodong},
  journal={Artificial Intelligence},
  pages={103905},
  year={2023},
  publisher={Elsevier}
}

Acknowledgments

We thank the list of contributors from the following open source repositories: MAPPO, HAPPO, safety-starter-agents, CMBPO.