Deep Reinforcement Learning for Dynamic Resource Allocation: Investigating the application of deep reinforcement learning for optimizing dynamic resource allocation problems

Abstract

Dynamic resource allocation is a critical challenge in various fields, including cloud computing, networking, and robotics, where resources need to be allocated efficiently to meet changing demands. Traditional approaches often struggle to adapt to dynamic environments due to their reliance on static policies. Deep reinforcement learning (DRL) has emerged as a promising technique for addressing dynamic resource allocation problems by learning optimal policies through interactions with the environment. This paper provides a comprehensive review of the application of DRL for dynamic resource allocation, highlighting its advantages, challenges, and future directions. We first discuss the basics of DRL and its relevance to resource allocation. We then review existing literature on DRL-based resource allocation approaches, categorizing them based on the nature of the resource allocation problem and the DRL algorithm used. Next, we discuss the challenges and limitations of current DRL approaches, such as scalability, sample inefficiency, and exploration-exploitation trade-offs. Finally, we present potential future research directions to address these challenges and further enhance the effectiveness of DRL for dynamic resource allocation.