Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework

• URL: http://arxiv.org/abs/2002.11883v2
• Date: Tue, 23 Feb 2021 12:05:04 GMT
• Title: Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework
• Authors: Ngoc Duy Nguyen, Thanh Thi Nguyen, Hai Nguyen, Doug Creighton, Saeid Nahavandi
• Abstract summary: This paper proposes a comprehensive software framework that plays a vital role in designing a connect-the-dots deep RL architecture. We have designed and developed a deep RL-based software framework that strictly ensures flexibility, robustness, and scalability.
• Score: 6.527722484694189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of deep learning to reinforcement learning (RL) has enabled RL to perform efficiently in high-dimensional environments. Deep RL methods have been applied to solve many complex real-world problems in recent years. However, development of a deep RL-based system is challenging because of various issues such as the selection of a suitable deep RL algorithm, its network configuration, training time, training methods, and so on. This paper proposes a comprehensive software framework 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. We have designed and developed a deep RL-based software framework that strictly ensures flexibility, robustness, and scalability. 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. To enforce generalization, the proposed architecture does not depend on a specific RL algorithm, a network configuration, the number of agents, or the type of agents. Using our framework, software developers can develop and integrate new RL algorithms or new types of agents, and can flexibly change network configuration or the number of agents.

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