NfgTransformer: Equivariant Representation Learning for Normal-form Games
<|reference_start|>NfgTransformer: Equivariant Representation Learning for Normal-form Games: Normal-form games (NFGs) are the fundamental model of strategic interaction. We study their representation using neural networks. We describe the inherent equivariance of NFGs -- any permutation of strategies describes an equivalent game -- as well as the challenges this poses for representation learning. We then propose the NfgTransformer architecture that leverages this equivariance, leading to state-of-the-art performance in a range of game-theoretic tasks including equilibrium-solving, deviation gain estimation and ranking, with a common approach to NFG representation. We show that the resulting model is interpretable and versatile, paving the way towards deep learning systems capable of game-theoretic reasoning when interacting with humans and with each other.<|reference_end|>
@article{liu2024nfgtransformer:,
    title={NfgTransformer: Equivariant Representation Learning for Normal-form
  Games},
    author={Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess},
    journal={arXiv preprint arXiv:2402.08393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08393},
    primaryClass={cs.GT}
}