Reinforcement learning (RL) has emerged as an effective approach for building an intelligent system, which involves multiple self-operated agents to collectively accomplish a designated task. More importantly, there has been a renewed focus on RL since the introduction of deep learning that essentially makes RL feasible to operate in high-dimensional environments. However, there are many diversified research directions in the current literature, such as multi-agent and multi-objective learning, and human-machine interactions. Therefore, in this paper, we propose a comprehensive software architecture that not only plays a vital role in designing a connect-the-dots deep RL architecture but also provides a guideline to develop a realistic RL application in a short time span. By inheriting the proposed architecture, software managers can foresee any challenges when designing a deep RL-based system. As a result, they can expedite the design process and actively control every stage of software development, which is especially critical in agile development environments. For this reason, we design a deep RL-based framework that strictly ensures flexibility, robustness, and scalability. To enforce generalization, the proposed architecture also does not depend on a specific RL algorithm, a network configuration, the number of agents, or the type of agents.
Bibtex:
@article{nguyen2023towards,
title={Towards designing a generic and comprehensive deep reinforcement learning framework},
author={Nguyen, Ngoc Duy and Nguyen, Thanh Thi and Pham, Nhat Truong and Nguyen, Hai and Nguyen, Dang Tu and Nguyen, Thanh Dang and Lim, Chee Peng and Johnstone, Michael and Bhatti, Asim and Creighton, Douglas and others},
journal={Applied Intelligence},
volume={53},
number={3},
pages={2967--2988},
year={2023},
publisher={Springer}
}