基于控制的强化学习马尔可夫决策过程框架

Q4 Computer Science

Performance Evaluation Review Pub Date : 2023-09-28 DOI:10.1145/3626570.3626585

Yingdong Lu, Mark S. Squillante, Chai Wah Wu

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引用次数: 0

摘要

多年来，强化学习(RL)已被证明在解决各种不确定性(DMuU)问题(包括与游戏和机器人控制相关的问题)下的学习和决策制定方面非常成功。为了解决这些问题，已经开发了许多不同的强化学习方法，取得了不同程度的成功。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Markov Decision Process Framework for Control-Based Reinforcement Learning

For many years, reinforcement learning (RL) has proven to be very successful in solving a wide variety of learning and decision making under uncertainty (DMuU) problems, including those related to game playing and robotic control. Many different RL approaches, with varying levels of success, have been developed to address these problems.

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来源期刊

Performance Evaluation Review Computer Science-Computer Networks and Communications

CiteScore

1.00

自引率

0.00%

发文量

193