Repo containing code for multi-agent deep reinforcement learning (MADRL).
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Latest commit a3651e2 Jul 31, 2018


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


This package requires both OpenAI Gym and a forked version of rllab (the multiagent branch). There are a number of other requirements which can be found in rllab/environment.yml file if using anaconda distribution.


The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository.

git clone --recursive

Then, add directories to PYTHONPATH

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.


Example run with curriculum:

python3 runners/ 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


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


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

  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},