Mutation-Bias Learning in Games
<|reference_start|>Mutation-Bias Learning in Games: We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. The intentional simplicity of one variant enables us to prove results on its relationship to a system of ordinary differential equations of replicator-mutator dynamics type, allowing us to present proofs on the algorithm's convergence conditions in various settings via its ODE counterpart. The more complicated variant enables comparisons to Q-learning based algorithms. We compare both variants experimentally to WoLF-PHC and frequency-adjusted Q-learning on a range of settings, illustrating cases of increasing dimensionality where our variants preserve convergence in contrast to more complicated algorithms. The availability of analytic results provides a degree of transferability of results as compared to purely empirical case studies, illustrating the general utility of a dynamical systems perspective on multi-agent reinforcement learning when addressing questions of convergence and reliable generalisation.<|reference_end|>
@article{bauer2024mutation-bias,
    title={Mutation-Bias Learning in Games},
    author={Johann Bauer, Sheldon West, Eduardo Alonso, Mark Broom},
    journal={arXiv preprint arXiv:2405.18190},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18190},
    primaryClass={cs.LG cs.MA math.DS math.OC q-bio.PE}
}