Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization

@inproceedings{ koelleICAART24,
    author      = "Michael Kölle and Felix Topp and Thomy Phan and Philipp Altmann and Jonas Nüßlein and Claudia Linnhoff-Popien",
    title       = "Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization",
    year        = "2024",
    
    
    abstract    = "Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new approach to Reinforcement Learning arises using the inherent properties of quantum mechanics, reducing the trainable parameters of a model significantly. However, gradient-based Multi-Agent Quantum Reinforcement Learning methods often have to struggle with barren plateaus, holding them back from matching the performance of classical approaches. We build upon an existing approach for gradient free Quantum Reinforcement Learning and propose three genetic variations with Variational Quantum Circuits for Multi-Agent Reinforcement Learning using evolutionary optimization. We evaluate our genetic variations in the Coin Game environment and also compare them to classical approaches. We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters. Compared to the larger neural network, our approaches archive similar results using 97.88% less parameters.",
    url         = "https://www.scitepress.org/Papers/2024/123828/123828.pdf",
    eprint      = "https://thomyphan.github.io/files/2024-icaart-preprint.pdf",
    
    publisher   = "SciTePress",
    booktitle   = "Proceedings of the 16th International Conference on Agents and Artificial Intelligence",
    pages       = "71--82",
    
    doi         = "https://doi.org/10.5220/0012382800003636"
}