Performance Optimization in Distributed Multi-Agent Reinforcement Learning on Cloud-Based Platforms

Dispensed multi-agent reinforcement learning (MARL) is an effective tool for tackling tough manage troubles by means of allowing coordination and cooperation among multiple agents. In cloud-based platforms, MARL can benefit from the availability of compute resources for education, but they face precise scalability and optimization-demanding situations. In this paper, we endorse a collection of methods for improving the overall performance of disbursed MARL in cloud-primarily based structures. We attention on two elements of overall performance optimization for scaling out distributed MARL: implementation optimization and optimization across a couple of stages of education. First, we advocate numerous strategies for enhancing the implementation of dispensed MARL, which include optimizing reminiscence usage and introducing size-aware optimizers. 2nd, we propose methods of optimizing allotted MARL across one-of-a-kind phases of education, inclusive of optimization for hyper parameter selection and optimizing education convergence. Our experiments display that our proposed methods improve the performance of distributed MARL in cloud-primarily based platforms, leading to higher scalability and quicker mastering.