论文标题
分发深入的强化学习：概述
Distributed Deep Reinforcement Learning: An Overview
论文作者
论文摘要
深度加强学习（DRL）是一个非常活跃的研究领域。但是，需要解决一些技术和科学问题，其中我们可以提及数据效率低下，探索 - 探索折衷和多任务学习。因此，引入了DRL的分布式修改；可以同时在许多机器上运行的代理。在本文中，我们对DRL分布式方法的作用进行了调查。我们通过研究关键研究工作对我们如何在DRL中使用分布式方法产生重大影响的关键研究作品来概述该领域的状态。我们选择从分布式学习的角度概述这些论文，而不是强化学习算法中创新的方面。此外，我们在不同的任务上评估了这些方法，并将其彼此以及单个演员和学习者代理人进行比较。
Deep reinforcement learning (DRL) is a very active research area. However, several technical and scientific issues require to be addressed, amongst which we can mention data inefficiency, exploration-exploitation trade-off, and multi-task learning. Therefore, distributed modifications of DRL were introduced; agents that could be run on many machines simultaneously. In this article, we provide a survey of the role of the distributed approaches in DRL. We overview the state of the field, by studying the key research works that have a significant impact on how we can use distributed methods in DRL. We choose to overview these papers, from the perspective of distributed learning, and not the aspect of innovations in reinforcement learning algorithms. Also, we evaluate these methods on different tasks and compare their performance with each other and with single actor and learner agents.
