多智能体强化学习的分布式框架研究

Yutai Zhou, Shawn Manuel, Peter Morales, Sheng Li, Jaime Peña, R. Allen
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引用次数: 0

摘要

在过去的几年里,深度强化学习领域的一些最重要的出版物都是由大规模分布式系统的大量计算推动的。随着计算量的增加,这些方法在一些复杂的电子游戏环境中取得了人类专家水平的表现,这促使人们进一步探索这些方法的局限性。在本文中,我们提出了一个为超级计算基础设施(如MIT SuperCloud)设计的分布式强化学习训练框架。我们回顾了一系列具有挑战性的学习环境,如谷歌研究足球,星际争霸II和多代理Mujoco,它们处于强化学习研究的前沿。我们提供了这些环境的结果,说明了这些问题的当前状态。最后,我们还通过列举在这些环境中进行的所有实验来量化和讨论执行强化学习研究所需的计算需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a Distributed Framework for Multi-Agent Reinforcement Learning Research
Some of the most important publications in deep reinforcement learning over the last few years have been fueled by access to massive amounts of computation through large scale distributed systems. The success of these approaches in achieving human-expert level performance on several complex video-game environments has motivated further exploration into the limits of these approaches as computation increases. In this paper, we present a distributed RL training framework designed for super computing infrastructures such as the MIT SuperCloud. We review a collection of challenging learning environments-such as Google Research Football, StarCraft II, and Multi-Agent Mujoco- which are at the frontier of reinforcement learning research. We provide results on these environments that illustrate the current state of the field on these problems. Finally, we also quantify and discuss the computational requirements needed for performing RL research by enumerating all experiments performed on these environments.
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