Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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DOI：
10.1007/978-981-13-8285-7_1
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Mohit Sewak
中科院分区：
文献类型：
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作者：
Mohit Sewak
在本章中，我们将讨论什么是强化学习以及它与人工智能的关系。然后，我们将尝试更深入地了解强化学习的基本组成部分，如状态、参与者、环境和奖励，并将尝试通过使用多个示例来了解每个方面的挑战，以便在进入一些高级主题之前，我们建立了良好的直觉，并建立了坚实的基础。我们还将讨论代理如何学习采取最佳操作以及学习相同操作的策略。我们还将学习On-Policy和Off-Policy方法之间的区别。
In this chapter, we will discuss what is Reinforcement Learning and its relationship with Artificial Intelligence. We would then try to go deeper to understand the basic building blocks of Reinforcement Learning like state, actor, environment, and the reward, and will try to understand the challenges in each of the aspect as revealed by using multiple examples so that the intuition is well established, and we build a solid foundation before going ahead into some advanced topics. We would also discuss how the agent learns to take the best action and the policy for learning the same. We will also learn the difference between the On-Policy and the Off-Policy methods.