The Role of Personalized Reward Mechanisms in Deep Reinforcement Learning Driven Cognitive Training: Applications, Challenges, and Future Directions

Qimiao Gao

2024

Abstract

Deep Reinforcement Learning (DRL) has revolutionized the field of cognitive training by integrating the decision-making capabilities of Reinforcement Learning (RL) and the perceptual power of Deep Learning (DL). A key component of DRL is the use of personalized reward mechanisms, which dynamically adjust the reinforcement signals to optimize individual learning trajectories. This review explores the application of personalized reward strategies, such as Q-learning, Advantage Actor-Critic (A3C), and Proximal Policy Optimization (PPO), in neurofeedback (NF) interventions for cognitive enhancement. We focus on their roles in treating conditions like attention deficit hyperactivity disorder (ADHD) and anxiety disorders and discuss their effectiveness in virtual reality-based cognitive training environments. Personalized reward mechanisms have shown significant potential in improving learning outcomes, engagement, and motivation by tailoring the difficulty and feedback of tasks to the user’s physiological and behavioral states. Despite these successes, challenges remain in Electroencephalography (EEG) data's real-time processing and personalized interventions' scalability across diverse populations. Future research should focus on improving the adaptability and generalization of these reward systems through multimodal data integration and advanced DRL techniques, while also addressing ethical concerns related to data privacy and user well-being.

Paper Citation

in Harvard Style

Gao Q. (2024). The Role of Personalized Reward Mechanisms in Deep Reinforcement Learning Driven Cognitive Training: Applications, Challenges, and Future Directions. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 543-551. DOI: 10.5220/0013528100004619

in Bibtex Style

@conference{daml24,
author={Qimiao Gao},
title={The Role of Personalized Reward Mechanisms in Deep Reinforcement Learning Driven Cognitive Training: Applications, Challenges, and Future Directions},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={543-551},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013528100004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - The Role of Personalized Reward Mechanisms in Deep Reinforcement Learning Driven Cognitive Training: Applications, Challenges, and Future Directions
SN - 978-989-758-754-2
AU - Gao Q.
PY - 2024
SP - 543
EP - 551
DO - 10.5220/0013528100004619
PB - SciTePress