Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning

• URL: http://arxiv.org/abs/2410.01739v2
• Date: Thu, 3 Oct 2024 23:18:17 GMT
• Title: Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning
• Authors: Xingrui Gu, Guanren Qiao, Chuyi Jiang, Tianqing Xia, Hangyu Mao,
• Abstract summary: We propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework. CBDQ enhances decision-making accuracy by endowing agents with human-like learning and reasoning capabilities. We evaluate the proposed method on discrete control benchmark tasks in various complicate environments.
• Score: 5.960184723807347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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