A Survey on Distributed Reinforcement Learning

In many settings, reinforcement learning (RL) has proven to be an effective tool for tackling difficult decision-making challenges. Traditional RL algorithms, on the other hand, frequently hit walls when confronted with issues of a sufficiently great scale or complexity. Distributed reinforcement learning (DRL) is a new area of study that hopes to circumvent these restrictions by dividing the learning workload among several computers. In this work, we offer a thorough overview of DRL, discussing its history, difficulties, applications, evaluation, scalability, and outstanding issues. We classify DRL approaches and frameworks and examine their similarities and differences. We also highlight the difficulties and restrictions of using DRL in real-world circumstances and examine its practical applicability in a variety of fields. We also describe current trends and future directions for evaluating DRL algorithms, and we analyse the performance of DRL algorithms on benchmark tasks. We also go through various options for distributed computing in DRL, as well as other methods for increasing the scalability and efficiency of DRL algorithms. We conclude by outlining key concerns and obstacles in DRL study, and by making suggestions for moving the subject forward. Overall, the goal of this survey is to give readers a picture of where things stand in terms of DRL study and application at the moment.