|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-intro-to-reinforcement-learning-using-robotics/
课程评论：没有评论
**课程名称：** 机器人强化学习实践入门 **课程概述：** 本课程旨在为初学者提供一个简单快捷地入门强化学习（RL）及其在机器人领域的应用。课程将强化学习的概念与机器人技术相结合，帮助学习者理解何时以及如何应用RL技术。 **核心内容：** * **强化学习基础：** 介绍RL的基本概念，包括智能体、环境、状态、动作、奖励等。 * **经典RL算法：** 讲解无需深度学习基础即可掌握和应用的经典RL算法。 * **深度强化学习简介：** 简要介绍深度强化学习的概念，但强调本课程侧重于基础入门，非高级内容。 * **RL在机器人中的应用：** 所有概念和解释都围绕机器人进行，旨在让学习者能直接将所学应用于机器人项目中。 * **动手实践：** 课程虽使用EV3 Mindstorms机器人套件进行实现，但即使没有该套件，学习者仍能理解概念并将其应用于任何机器人平台。 **课程目标：** * 为零基础的学习者提供一个易于理解的RL入门。 * 使学习者能够了解RL的基本原理并将其应用于机器人项目。 * 激发学习者对RL的兴趣，鼓励进一步深入学习。 **学习成果：** * 学习者将能够编程机器人，使其在环境中表现出适当的行为，而无需明确的指令。 * 学习者将理解RL在机器人领域的基本应用场景。 **注意：** 本课程不是高级课程，不教授如何构建超乎寻常能力的机器人，而是专注于为入门 RL 提供坚实的基础。
Diving into Reinforcement Learning can seem daunting if you don't have the proper hands-on guidance. Many times, people have asked me whether they should master Deep Learning before delving into Reinforcement Learning and my answer has always been that "it depends on what you want to do with RL". RL is a broad domain in its own respect. There are classical RL algorithms that can be learned and applied without any Deep Learning experience. There is also Deep Reinforcement Learning which leverages neural networks to help RL agents to learn proper behaviors in their environment through trials-and-error with reward functions.This course has been designed to be the easiest and fastest basic entry point into RL and its applications in Robotics. From the first to the last videos, I explain every concept in RL in the context of robotics. I am intentional about this because I want to empower you to readily know when and how to apply RL techniques in Robotics. It is not an advanced course. Instead, it is your best option when you are getting started in RL (without any prior knowledge) and you are interested in being able to readily apply what you learn in your robotic projects.Even though I use sensors and actuators from the EV3 Mindstorms robotics kit in the hands-on implementation sessions, you don't necessarily have to purpose the kit to get the best out of this course. It will certainly enhance your learning experience if you have the kit but don't worry if you don't. Without the kit, you can still understand the concepts and apply them on any robotic platform. It is my desire that after finishing this course, your passion for RL will be ignited and you will study further to know about more advanced algorithms and techniques.So, while this course won't teach you how to build super-human capabilities into your robotics projects, you will certainly learn how to program robots to behave well in their environments without explicit instructions on what's considered "proper behavior".
