开始时间: 12/21/2023 持续时间: 4-6 hours/week
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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/complete-reinforcement-learning-system
课程评论：没有评论
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 capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution.
Name:Welcome to the Final Capstone Course!
Description:Welcome to the final capstone course of the Reinforcement Learning Specialization!!
Name:Milestone 1: Formalize Word Problem as MDP
Description:This week you will read a description of a problem, and translate it into an MDP. You will complete skeleton code for this environment, to obtain a complete MDP for use in this capstone project.
Name:Milestone 2: Choosing The Right Algorithm
Description:This week you will select from three algorithms, to learn a policy for the environment. You will reflect on and discuss the appropriateness of each algorithm for this environment.
Name:Milestone 3: Identify Key Performance Parameters
Description:This week you will identify key parameters that affect the performance of your agent. The goal is to understand the space of options, to later enable you to choose which parameter you will investigate in-depth for your agent.
Name:Milestone 4: Implement Your Agent
Description:This week, you will implement your agent using Expected Sarsa or Q-learning with RMSProp and Neural Networks. To use NNs, you will have to use a more careful stepsize selection strategy, which is why you will use RMSProp. You will also verify the correctness of your agent.
Name:Milestone 5: Submit Your Parameter Study!
Description:This week you will identify a parameter to study, for your agent. Once you select the parameter to study, we will provide you with a range of values and specific values for other parameters. You will write a script to run your agent and environment on the set of parameters, to determine performance across these parameters. You will gain insight into the impact of parameters on agent performance. You will also get to visualize the agents that you learn. Your parameter study will consist of an array of values that we will check for correctness.
在这最后一门课程中，您将把您从课程 1、2 和 3 中学到的知识结合起来，以实施针对问题的完整 RL 解决方案。这个顶点将让您了解每个组件——问题制定、算法选择、参数选择和表示设计——如何组合成一个完整的解决方案，以及在现实世界中部署 RL 时如何做出适当的选择。该项目将要求您实现激发问题的环境和具有神经网络函数逼近的控制代理。此外，您将对您的学习系统进行科学研究，以提高您评估 RL 代理稳健性的能力。要在现实世界中使用 RL，至关重要的是 (a) 将问题适当地形式化为 MDP，(b) 选择适当的算法，(c) 确定实现中的哪些选择将对性能产生重大影响，以及 (d) 验证算法的预期行为。对于计划使用 RL 解决实际问题的任何人来说，这个顶点都很有价值。要在本课程中取得成功，您需要完成本专业的课程 1、2 和 3 或同等课程。在本课程结束时，您将能够： 完成问题的 RL 解决方案，从问题制定、适当的算法选择和实施以及对解决方案有效性的实证研究开始。
