A finite time analysis of distributed Q-learning
<|reference_start|>A finite time analysis of distributed Q-learning: Multi-agent reinforcement learning (MARL) has witnessed a remarkable surge in interest, fueled by the empirical success achieved in applications of single-agent reinforcement learning (RL). In this study, we consider a distributed Q-learning scenario, wherein a number of agents cooperatively solve a sequential decision making problem without access to the central reward function which is an average of the local rewards. In particular, we study finite-time analysis of a distributed Q-learning algorithm, and provide a new sample complexity result of $\tilde{\mathcal{O}}\left( \min\left\{\frac{1}{\epsilon^2}\frac{t_{\text{mix}}}{(1-\gamma)^6 d_{\min}^4 } ,\frac{1}{\epsilon}\frac{\sqrt{|\gS||\gA|}}{(1-\sigma_2(\boldsymbol{W}))(1-\gamma)^4 d_{\min}^3} \right\}\right)$ under tabular lookup<|reference_end|>
@article{lim2024a,
    title={A finite time analysis of distributed Q-learning},
    author={Han-Dong Lim, Donghwan Lee},
    journal={arXiv preprint arXiv:2405.14078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14078},
    primaryClass={cs.AI cs.LG cs.MA}
}