A Survey on Distributed Reinforcement Learning

Abstract

Reinforcement learning (RL) has shown remarkable success in solving complex decision-making problems in various domains. However, traditional RL algorithms are often limited by their inability to handle large-scale and complex problems. Distributed reinforcement learning (DRL) is an emerging research field that aims to address these limitations by distributing the learning process across multiple agents or machines. In this paper, we provide a comprehensive survey of DRL, including its background, challenges, applications, evaluation, scalability, and open problems. We present a taxonomy of DRL methods and frameworks, and provide a comparative analysis of different DRL techniques. We also discuss the real-world applications of DRL in various domains, and highlight the challenges and limitations of applying DRL in practical scenarios. Furthermore, we evaluate the performance of DRL algorithms on benchmark tasks, and discuss current trends and future directions for evaluating DRL algorithms. We also discuss the techniques for improving the scalability and efficiency of DRL algorithms, including the approaches for distributed computing in DRL. Finally, we identify critical issues and challenges in DRL research, and provide recommendations for future research in this field. Overall, this survey aims to provide a comprehensive overview of the current state-of-the-art in DRL research and its applications.