|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fundamentals-of-reinforcement-learning
课程评论：没有评论
课程名称：强化学习基础 概述：强化学习是机器学习的一个子领域，同时也是自动决策和人工智能的一种通用形式。本课程将介绍统计学习技术，让学员了解代理如何明确采取行动并与世界互动。理解学习代理在决策中的重要性和面临的挑战对于当今越来越多关注交互代理和智能决策的公司至关重要。完成本课程后，学员将能够： - 将问题形式化为马尔可夫决策过程（MDP） - 理解基本的探索方法及探索与利用之间的权衡 - 理解价值函数，作为实现最佳决策的通用工具 - 掌握如何将动态规划作为解决工业控制问题的有效方法。 本课程教授强化学习的关键概念，涵盖经典和现代的强化学习算法，完成课程后，学员将能够开始在实际问题中应用强化学习，在这些问题中，学员需要指定或已有MDP模型。 这是强化学习专业化系列课程的第一门课。 课程大纲： 1. 课程介绍：欢迎来到强化学习基础课程，这是由阿尔伯塔大学、Onlea和Coursera联合推出的四部分强化学习专业化课程的第一部分。本模块将介绍授课教师，让学员初步了解课程内容，并提供详细的学习路线图，以帮助学员顺利完成整个专业化课程。 2. 顺序决策引言：在本周，学员将学习如何理解顺序决策中的探索-利用权衡，实施增量算法以估计行动值，并比较不同探索算法的优缺点。本周的评估任务是实施和测试一个epsilon-greedy代理。 3. 马尔可夫决策过程：在面对工业问题时，首先也是最重要的一步是将问题转化为马尔可夫决策过程（MDP）。这个转化的质量直接关系到解决方案的优劣。本周将学习MDP的定义、目标导向行为以及如何通过最大化标量奖励获得这一行为，还将理解回合任务与继续任务之间的区别。本周的评估任务是创建三个符合MDP框架的示例任务。 4. 价值函数 & 贝尔曼方程：一旦将问题制定为MDP，使用价值函数寻找最优政策会更高效。本周将学习政策和价值函数的定义，以及贝尔曼方程，这是所有算法的关键技术。 5. 动态规划：本周将学习如何在拥有MDP模型的情况下计算价值函数和最优政策。学员将实施动态规划来计算价值函数和最优政策，并理解动态规划在工业应用中的实用性。同时，学员将学习通用政策迭代，作为构建最大化回报算法的通用模板。本周的评估任务是实现一个高效的动态规划代理，在模拟的工业控制问题中进行操作。
Name:Welcome to the Course!
Description:Welcome to: Fundamentals of Reinforcement Learning, the first course in a four-part specialization on Reinforcement Learning brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, get a flavour of what the course has in store for you, and be given an in-depth roadmap to help make your journey through this specialization as smooth as possible.
Name:An Introduction to Sequential Decision-Making
Description:For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.
Name:Markov Decision Processes
Description:When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.
Name:Value Functions & Bellman Equations
Description:Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.
Name:Dynamic Programming
Description:This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.
Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization.
