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LMAC

Official PyTorch implementation of LMAC from the paper: Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games.

We offer trained models and test code for 2D-RPS visualisation and Kuhn Poker->Leduc Poker Generalization test. Full training and testing code will be published soon.

How to run

Firstly create and activate the required conda environment.

conda env create -f lmac.yml
conda activate lmac

2D-RPS Testing

123 Visualisation results can be tested in 2d_rps/visualization_2d_rps.ipynb.

Kuhn->Leduc Generalization

123

we provide a local implementation in which one can reproduce the results of generalising our models trained on Kuhn Poker to Leduc Poker.

cd leduc_poker
# To reproduce the approximate best-response results
python3 kuhn_to_leduc.py --br_type 'approx_br_rand'
# To reproduce the exact best-response results
python3 kuhn_to_leduc.py --br_type 'exact_br'

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