Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning
<|reference_start|>Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning: Reinforcement learning encounters challenges in various environments related to robustness and explainability. Traditional Q-learning algorithms cannot effectively make decisions and utilize the historical learning experience. To overcome these limitations, we propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework, enhancing decision-making accuracy by endowing agents with human-like learning and reasoning capabilities. Drawing inspiration from cognitive science, our method maintains a subjective belief distribution over the expectation of actions, leveraging a cluster-based subjective belief model that enables agents to reason about the potential probability associated with each decision. CBDQ effectively mitigates overestimated phenomena and optimizes decision-making policies by integrating historical experiences with current contextual information, mimicking the dynamics of human decision-making. We evaluate the proposed method on discrete control benchmark tasks in various complicate environments. The results demonstrate that CBDQ exhibits stronger adaptability, robustness, and human-like characteristics in handling these environments, outperforming other baselines. We hope this work will give researchers a fresh perspective on understanding and explaining Q-learning.<|reference_end|>
@article{gu2024mimicking,
    title={Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning},
    author={Xingrui Gu, Guanren Qiao, Chuyi Jiang, Tianqing Xia, Hangyu Mao},
    journal={arXiv preprint arXiv:2410.01739},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01739},
    primaryClass={cs.AI cs.LG}
}