A Complete Reinforcement Learning System (Capstone)

Go to Course: https://www.coursera.org/learn/complete-reinforcement-learning-system

Introduction

### Course Review: A Complete Reinforcement Learning System (Capstone) **Overview:** As the culminating course in the Reinforcement Learning Specialization on Coursera, "A Complete Reinforcement Learning System (Capstone)" offers a hands-on opportunity for learners to apply the knowledge they have acquired throughout the previous courses. This capstone project is designed not only to solidify your understanding of key concepts, but also to provide practical experience in implementing a reinforcement learning (RL) solution from scratch. The course intricately weaves together aspects such as problem formulation, algorithm selection, parameter tuning, and representation design, encapsulating the journey of creating an RL system. **What You Will Learn:** The capstone course is structured around five significant milestones, each designed to help you build a robust RL system: 1. **Formalize Word Problem as MDP:** You will gain experience in translating real-world problems into a Markov Decision Process (MDP). This foundational step is crucial for understanding how RL algorithms operate. 2. **Choosing The Right Algorithm:** Delve into the complexities of RL algorithms by evaluating three distinct approaches, emphasizing the importance of selecting the right method for your environment. 3. **Identify Key Performance Parameters:** Understanding the parameters that drive the performance of your agent is critical. This milestone focuses on deepening your comprehension of these variables and their implications on your model's efficacy. 4. **Implement Your Agent:** Practical implementation is where the understanding transitions to application. You'll create your agent using Expected Sarsa or Q-learning, utilizing sophisticated techniques like RMSProp and Neural Networks. 5. **Submit Your Parameter Study:** Lastly, this milestone involves conducting a parameter study to evaluate how variations affect agent performance, culminating in valuable insights and visualizations. **Course Highlights:** - **Accessibility of Content:** The course is well-structured with a progressive learning approach. Each milestone builds upon the last, making complex concepts more accessible. - **Hands-On Experience:** The project-centric structure enables students to engage directly with RL principles, ensuring they don’t just learn theories, but also apply them in practical scenarios. - **Community and Support:** Participants are encouraged to engage with peers through forums, facilitating knowledge-sharing and collaborative problem-solving. **Who Should Take This Course:** This course is recommended for anyone who has completed the preceding courses in the Reinforcement Learning Specialization and is looking to apply their knowledge in real-world contexts. It is particularly beneficial for: - Data Scientists interested in applying machine learning in dynamic environments. - Software Engineers looking to deepen their expertise in AI and machine learning. - Researchers aiming to explore innovative applications of reinforcement learning. **Conclusion:** I highly recommend "A Complete Reinforcement Learning System (Capstone)" for anyone eager to deepen their understanding of reinforcement learning while gaining practical experience. This course successfully bridges theory and application, allowing you to create a complete RL solution and prepare for real-world challenges. The comprehensive milestones ensure that you walk away with tangible skills that can be applied to future projects, whether in academia or industry. Embrace this opportunity to strengthen your portfolio in the exciting field of reinforcement learning!

Syllabus

Welcome to the Final Capstone Course!

Welcome to the final capstone course of the Reinforcement Learning Specialization!!

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.

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.

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.

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.

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.