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.
A Complete Reinforcement Learning System (Capstone)
This course is part of Reinforcement Learning Specialization
Instructors: Martha White
Sponsored by Louisiana Workforce Commission
22,341 already enrolled
(630 reviews)
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Skills you'll gain
- Statistical Modeling
- Machine Learning Methods
- Statistical Machine Learning
- Applied Machine Learning
- Artificial Neural Networks
- Probability & Statistics
- Time Series Analysis and Forecasting
- Artificial Intelligence and Machine Learning (AI/ML)
- Predictive Analytics
- Artificial Intelligence
- Reinforcement Learning
- Engineering Software
- Simulation and Simulation Software
- Predictive Modeling
- Machine Learning Algorithms
- Computer Science
- Machine Learning
- Markov Model
- Deep Learning
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There are 6 modules in this course
Welcome to the final capstone course of the Reinforcement Learning Specialization!!
What's included
2 videos2 readings1 discussion prompt
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.
What's included
4 videos1 programming assignment
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.
What's included
7 videos1 assignment
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.
What's included
4 videos1 assignment
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.
What's included
6 videos1 programming assignment
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.
What's included
6 videos1 programming assignment
Instructors
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Reviewed on Jun 16, 2021
Very good course and specialization. If you want to get the most out of it, I recommend following their required reading and keep reading that book to cover other chapter as well.
Reviewed on Feb 3, 2021
Good project as a capstone. Wish there would have been more work needed from our side of things in terms of coding, but very solid final course for RL.
Reviewed on Apr 27, 2020
This is the final chapter. It is one of the easiest and it was fun doing that lunar landing project. This specialisation is the best for a person taking baby steps in the reinforcement learning.
Recommended if you're interested in Data Science
EIT Digital
University of Colorado Boulder
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