A Platform for Many-agent Reinforcement Learning
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merrymercy Merge pull request #40 from TimerChen/master
Fix some bugs. Improve render
Latest commit 92256aa Sep 25, 2018


Build Status stability-experimental

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.


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 git@github.com:geek-ai/MAgent.git
cd MAgent

sudo apt-get install cmake libboost-system-dev libjsoncpp-dev libwebsocketpp-dev

bash build.sh
export PYTHONPATH=$(pwd)/python:$PYTHONPATH

Install on OSX

Note: There is an issue with homebrew for installing websocketpp, please refer to #17

git clone git@github.com:geek-ai/MAgent.git
cd MAgent

brew install cmake llvm boost
brew install jsoncpp argp-standalone
brew tap david-icracked/homebrew-websocketpp
brew install --HEAD david-icracked/websocketpp/websocketpp

bash build.sh
export PYTHONPATH=$(pwd)/python:$PYTHONPATH


Get started


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/.


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.


Many thanks to Tianqi Chen for the helpful suggestions.