Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
<|reference_start|>Decision Theory-Guided Deep Reinforcement Learning for Fast Learning: This paper introduces a novel approach, Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL), to address the inherent cold start problem in DRL. By integrating decision theory principles, DT-guided DRL enhances agents' initial performance and robustness in complex environments, enabling more efficient and reliable convergence during learning. Our investigation encompasses two primary problem contexts: the cart pole and maze navigation challenges. Experimental results demonstrate that the integration of decision theory not only facilitates effective initial guidance for DRL agents but also promotes a more structured and informed exploration strategy, particularly in environments characterized by large and intricate state spaces. The results of experiment demonstrate that DT-guided DRL can provide significantly higher rewards compared to regular DRL. Specifically, during the initial phase of training, the DT-guided DRL yields up to an 184% increase in accumulated reward. Moreover, even after reaching convergence, it maintains a superior performance, ending with up to 53% more reward than standard DRL in large maze problems. DT-guided DRL represents an advancement in mitigating a fundamental challenge of DRL by leveraging functions informed by human (designer) knowledge, setting a foundation for further research in this promising interdisciplinary domain.<|reference_end|>
@article{wan2024decision,
    title={Decision Theory-Guided Deep Reinforcement Learning for Fast Learning},
    author={Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua,
  Munindar P. Singh},
    journal={arXiv preprint arXiv:2402.06023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06023},
    primaryClass={cs.LG cs.AI cs.GT}
}