GitHub - geek-ai/MAgent: A Platform for Many-agent Reinforcement Learning
source link: https://github.com/geek-ai/MAgent
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This project is no longer maintained
Please see https://github.com/Farama-Foundation/MAgent for a maintained fork of this project that's installable with pip.
MAgent
MAgent is a research platform for many-agent reinforcement learning. Unlike previous research platforms that focus on reinforcement learning research with a single agent or only few agents, MAgent aims at supporting reinforcement learning research that scales up from hundreds to millions of agents.
Requirement
MAgent supports Linux and OS X running Python 2.7 or python 3. We make no assumptions about the structure of your agents. You can write rule-based algorithms or use deep learning frameworks.
Install on Linux
git clone [email protected]:geek-ai/MAgent.git
cd MAgent
sudo apt-get install cmake libboost-system-dev libjsoncpp-dev libwebsocketpp-dev
bash build.sh
export PYTHONPATH=$(pwd)/python:$PYTHONPATHInstall on OSX
Note: There is an issue with homebrew for installing websocketpp, please refer to #17
git clone [email protected]:geek-ai/MAgent.git
cd MAgent
brew install cmake llvm [email protected]
brew install jsoncpp argp-standalone
brew tap david-icracked/homebrew-websocketpp
brew install --HEAD david-icracked/websocketpp/websocketpp
brew link --force [email protected]
bash build.sh
export PYTHONPATH=$(pwd)/python:$PYTHONPATHExamples
The training time of following tasks is about 1 day on a GTX1080-Ti card. If out-of-memory errors occur, you can tune infer_batch_size smaller in models.
Note : You should run following examples in the root directory of this repo. Do not cd to examples/.
Train
Three examples shown in the above video. Video files will be saved every 10 rounds. You can use render to watch them.
-
pursuit
python examples/train_pursuit.py --train -
gathering
python examples/train_gather.py --train -
battle
python examples/train_battle.py --train
An interactive game to play with battle agents. You will act as a general and dispatch your soldiers.
- battle game
python examples/show_battle_game.py
Baseline Algorithms
The baseline algorithms parameter-sharing DQN, DRQN, a2c are implemented in Tensorflow and MXNet. DQN performs best in our large number sharing and gridworld settings.
Acknowledgement
Many thanks to Tianqi Chen for the helpful suggestions.
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