Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning
<|reference_start|>Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning: Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). A promising approach is to learn a meaningful latent representation space through auxiliary learning objectives alongside the MARL objective to aid in learning a successful control policy. In our work, we present MAPO-LSO (Multi-Agent Policy Optimization with Latent Space Optimization) which applies a form of comprehensive representation learning devised to supplement MARL training. Specifically, MAPO-LSO proposes a multi-agent extension of transition dynamics reconstruction and self-predictive learning that constructs a latent state optimization scheme that can be trivially extended to current state-of-the-art MARL algorithms. Empirical results demonstrate MAPO-LSO to show notable improvements in sample efficiency and learning performance compared to its vanilla MARL counterpart without any additional MARL hyperparameter tuning on a diverse suite of MARL tasks.<|reference_end|>
@article{huh2024representation,
    title={Representation Learning For Efficient Deep Multi-Agent Reinforcement
  Learning},
    author={Dom Huh, Prasant Mohapatra},
    journal={arXiv preprint arXiv:2406.02890},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02890},
    primaryClass={cs.MA cs.AI cs.LG}
}