# MADRL

**Repository Path**: mingyuxu/MADRL

## Basic Information

- **Project Name**: MADRL
- **Description**: The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-16
- **Last Updated**: 2022-03-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MADRL

**Note**: 
**A maintained version of the first three environments with various fixes is included with PettingZoo (https://github.com/PettingZoo-Team/PettingZoo, https://www.pettingzoo.ml/sisl)**

This package provides implementations of the following multi-agent reinforcement learning environemnts:

- [Pursuit Evastion](https://github.com/sisl/MADRL/blob/master/madrl_environments/pursuit/pursuit_evade.py)
- [Waterworld](https://github.com/sisl/MADRL/blob/master/madrl_environments/pursuit/waterworld.py)
- [Multi-Agent Walker](https://github.com/sisl/MADRL/blob/master/madrl_environments/walker/multi_walker.py)
- [Multi-Ant](https://github.com/sisl/MADRL/blob/master/madrl_environments/mujoco/ant/multi_ant.py)


## Requirements

This package requires both [OpenAI Gym](https://github.com/openai/gym) and a forked version of [rllab](https://github.com/rejuvyesh/rllab/tree/multiagent) (the multiagent branch). There are a number of other requirements which can be found
in `rllab/environment.yml` file if using `anaconda` distribution.

## Setup

The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository.
```bash
git clone --recursive git@github.com:sisl/MADRL.git
```

Then, add directories to `PYTHONPATH`
```bash
export PYTHONPATH=$(pwd):$(pwd)/rltools:$(pwd)/rllab:$PYTHONPATH
```

Install the required dependencies. Good idea is to look into `rllab/environment.yml` file if using `anaconda` distribution.

## Usage

Example run with curriculum:

```bash
python3 runners/run_multiwalker.py rllab \ # Use rllab for training
    --control decentralized \ # Decentralized training protocol
    --policy_hidden 100,50,25 \ # Set MLP policy hidden layer sizes
    --n_iter 200 \ # Number of iterations
    --n_walkers 2 \ # Starting number of walkers
    --batch_size 24000 \ # Number of rollout waypoints
    --curriculum lessons/multiwalker/env.yaml
```

## Details

Policy definitions exist in `rllab/sandbox/rocky/tf/policies`.

## Citation

Please cite the accompanied paper, if you find this useful:

```
@inproceedings{gupta2017cooperative,
  title={Cooperative multi-agent control using deep reinforcement learning},
  author={Gupta, Jayesh K and Egorov, Maxim and Kochenderfer, Mykel},
  booktitle={International Conference on Autonomous Agents and Multiagent Systems},
  pages={66--83},
  year={2017},
  organization={Springer}
}
```