Distributed Deep Reinforcement Learning: An Overview

Autor:	Samsami, Mohammad Reza, Alimadad, Hossein
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 15 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.11012 Zobrazit plný text záznamu View this record from Arxiv