基于生物反馈的递归学习系统 (BRRLS)

Abstract

This paper proposes a novel recursive learning system (BRRLS) that leverages biofeedback to enable adaptive learning. The core idea is to utilize real-time monitoring of physiological signals, such as heart rate and electroencephalography (EEG), to construct a system capable of dynamically adjusting its learning strategy. The system employs reinforcement learning algorithms, where the biofeedback signals serve as both learning objectives and feedback signals. This allows for the continuous optimization of learning parameters and strategies, leading to improved performance on complex tasks. The novelty of BRLS lies in its direct integration of biological feedback mechanisms to imbue the learning system with self-regulatory capabilities, mirroring biological learning processes. This approach has significant potential applications in areas such as robotic control and human-computer interaction. The system's architecture incorporates a feedback loop designed for iterative improvement, fundamentally distinguishing it from traditional, static learning models. Mathematical formulations detail the key components and operational principles of the BRRLS, emphasizing the role of state estimation, reward function design, and policy optimization within the reinforcement learning framework. The system is designed for modularity, allowing for the integration of diverse biofeedback modalities and reinforcement learning algorithms. Future research will focus on scaling the system to handle more complex tasks and exploring the potential for transferring learned strategies to different environments.