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所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/specializations/reinforcement-learning
课程评论：没有评论
强化学习专业化课程总结 课程名称：强化学习专业化 概述：强化学习专业化包含四门课程，深入探索自适应学习系统和人工智能（AI）的潜力。学习者将了解到，充分利用人工智能需要掌握自适应学习系统。通过反复尝试和错误的互动，强化学习（RL）解决方案能够帮助我们解决现实问题。本课程将指引学习者从头到尾实现一个完整的RL解决方案。 课程结束时，学习者将掌握现代概率人工智能的基础知识，具备参加更高级课程的能力，或将AI工具和思想应用于实际问题。课程将聚焦于“小规模”问题，以帮助学员理解强化学习的基础，授课的专家来自阿尔伯塔大学理学院，皆为世界知名的领域专家。 本专业化课程所教授的工具可广泛应用于多个领域，包括游戏开发（AI）、客户互动（如网站与客户的 взаимодействие）、智能助手、推荐系统、供应链、工业控制、金融、石油和天然气管道及工业控制系统等。 课程大纲： 1. **强化学习基础** 学习强化学习作为机器学习的子领域和一种自动决策及AI的通用形式。 课程链接：[强化学习基础](https://www.coursera.org/learn/fundamentals-of-reinforcement-learning) 2. **基于样本的学习方法** 理解多种算法，这些算法能根据与环境的反复交互学习近乎最优的策略。 课程链接：[基于样本的学习方法](https://www.coursera.org/learn/sample-based-learning-methods) 3. **使用函数逼近进行预测和控制** 学习解决大规模、高维和潜在无限状态空间的问题。 课程链接：[预测与控制函数逼近](https://www.coursera.org/learn/prediction-control-function-approximation) 4. **完整的强化学习系统（顶点项目）** 综合前面三门课程的知识，实施一个完整的RL解决方案。 课程链接：[完整的强化学习系统](https://www.coursera.org/learn/complete-reinforcement-learning-system) 通过这四门课程，学习者将具备构建和应用强化学习系统的能力，为更复杂的AI应用奠定坚实基础。
Course Link: https://www.coursera.org/learn/fundamentals-of-reinforcement-learning
Name:Fundamentals of Reinforcement Learning
Description:Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This ... Enroll for free.
Course Link: https://www.coursera.org/learn/sample-based-learning-methods
Name:Sample-based Learning Methods
Description:In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the ... Enroll for free.
Course Link: https://www.coursera.org/learn/prediction-control-function-approximation
Name:Prediction and Control with Function Approximation
Description:In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that ... Enroll for free.
Course Link: https://www.coursera.org/learn/complete-reinforcement-learning-system
Name:A Complete Reinforcement Learning System (Capstone)
Description:In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This ... Enroll for free.
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.
强化学习专业化：强化学习专业化包含4门课程，探讨自适应学习系统和人工智能（AI）的强大功能。 充分利用人工智能的潜力需要自适应学习系统。了解强化学习（RL）解决方案如何通过反复尝试和错误互动，通过从头到尾实施一个完整的RL解决方案来帮助解决实际问题。 到本专业课程结束时，学习者将了解现代概率人工智能（AI）的基础，并准备参加更高级的课程或将AI工具和思想应用于实际问题。该内容将重点关注“小规模”问题，以了解强化学习的基础，这是由阿尔伯塔大学理学院的世界知名专家教授的。 在本专业知识中学习的工具可以应用于游戏开发（AI），客户互动（网站与客户互动的方式），智能助手，推荐系统，供应链，工业控制，金融，石油和化工。天然气管道，工业控制系统等。
