SLS-BRD: A system-level approach to seeking generalised feedback Nash equilibria
<|reference_start|>SLS-BRD: A system-level approach to seeking generalised feedback Nash equilibria: This work proposes a policy learning algorithm for seeking generalised feedback Nash equilibria in $N_P$-players non-cooperative dynamic games. We consider linear-quadratic games with stochastic dynamics and design a best-response dynamics in which players update and communicate a parametrisation of their state-feedback policies. Our approach leverages the System Level Synthesis framework to formulate each player's update rule as the solution to a tractable robust optimisation problem. Under certain conditions, rates of convergence to a feedback Nash equilibrium can be established. The algorithm is showcased in exemplary problems ranging from the decentralised control of unstable systems to competition in oligopolistic markets.<|reference_end|>
@article{neto2024sls-brd:,
    title={SLS-BRD: A system-level approach to seeking generalised feedback Nash
  equilibria},
    author={Otacilio B. L. Neto, Michela Mulas, Francesco Corona},
    journal={arXiv preprint arXiv:2404.03809},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03809},
    primaryClass={math.OC cs.SY eess.SY}
}