Evolution of strategies in iterated games: a deep multi-agent reinforcement learning approach with hybrid genetic and CMA-ES optimization

ShangJun Li, Avijit Deb Nath, Md. Hadi Al-amin

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DOI: 10.3389/frai.2026.1844089
Published: 2026-06-22
Container: Frontiers in Artificial Intelligence
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/frai.2026.1844089,
  title = {Evolution of strategies in iterated games: a deep multi-agent reinforcement learning approach with hybrid genetic and CMA-ES optimization},
  author = {ShangJun Li and Avijit Deb Nath and Md. Hadi Al-amin},
  year = {2026},
  journal = {Frontiers in Artificial Intelligence},
  doi = {10.3389/frai.2026.1844089},
  url = {https://doi.org/10.3389/frai.2026.1844089}
}

RIS

TY  - JOUR
TI  - Evolution of strategies in iterated games: a deep multi-agent reinforcement learning approach with hybrid genetic and CMA-ES optimization
AU  - ShangJun Li
AU  - Avijit Deb Nath
AU  - Md. Hadi Al-amin
PY  - 2026
JO  - Frontiers in Artificial Intelligence
DO  - 10.3389/frai.2026.1844089
UR  - https://doi.org/10.3389/frai.2026.1844089
ER  -

APA

Li, S., Nath, A. D., & Al-amin, M. H. (2026). Evolution of strategies in iterated games: a deep multi-agent reinforcement learning approach with hybrid genetic and CMA-ES optimization. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2026.1844089

Source records

crossref · retrieved 2026-11-08T08:50:49.306Z