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 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.
What you will learn
Welcome to the Final Capstone Course!
Welcome to the final capstone course of the Reinforcement Learning Specialization!!
Milestone 1: Formalize Word Problem as MDP
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.
Milestone 2: Choosing The Right Algorithm
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.
Milestone 3: Identify Key Performance Parameters
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.
