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Local Information Agent Modelling (LIAM)

Source code of our LIAM implementation in the double speaker-listener environment. The code is written in python 3, using Pytorch for the implementation of the deep networks. Other important packages are OpenAI Baselines, OpenAI gym, and the Multi-agent Particle Environment.

Installation

virtualenv -p python3 LIAM_ENV
cd LIAM_ENV
source bin/activate
pip install torch
pip install tensorflow
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .
cd ..
git clone https://github.com/shariqiqbal2810/multiagent-particle-envs.git
cd multiagent-particle-envs
pip install -e .
cd ..
pip install gym==0.9.4
pip install seaborn
git clone [email protected]:uoe-agents/LIAM.git
cd LIAM
cp multi_agent_env/simple_reference.py ../multiagent-particle-envs/multiagent/scenarios/.

Execution

python run_tests.py 0

Citating LIAM

If you use this repository in your work, please consider citing the LIAM paper

@article{papoudakis2021local,
  title={Local Information Agent Modelling in Partially-Observable Environments},
  author={Papoudakis, Georgios and Christianos, Filippos and Albrecht, Stefano V.},
  journal={arXiv preprint arXiv:2006.09447},
  year={2021}
}

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Contributors

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