Three Ways to Integrate Microservices Into MARL

Microservice architecture is a key component of modern distributed software systems, providing scalability and independent deployment of individual components. However, many different factors, such as the dynamic variability of modern Internet services and the evolution of user requests, can offset the advantages of microservices. Machine learning algorithms, in particular deep reinforcement learning, are an effective way to optimize distributed software systems, allowing adaptive management of system resources in non-stationary environments. This paper discusses the integration of multi-agent reinforcement learning into a micro service architecture. The paper demonstrates the results of training intelligent agents in the road selection service based on the current weather condition. Three methods of integrating deep Q-Iearning into the microservice architecture were developed, and three classical algorithms of deep reinforcement learning were used to evaluate the effectiveness of the methods.