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.
Este curso forma parte de Programa especializado: Aprendizaje por refuerzo
Ofrecido Por
Programa especializado: Aprendizaje por refuerzoUniversidad de AlbertaAcerca de este Curso
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Habilidades que obtendrás
- Artificial Intelligence (AI)
- Machine Learning
- Reinforcement Learning
- Function Approximation
- Intelligent Systems
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Programa - Qué aprenderás en este curso
Welcome to the Final Capstone Course!
Milestone 1: Formalize Word Problem as MDP
Milestone 2: Choosing The Right Algorithm
Milestone 3: Identify Key Performance Parameters
Reseñas
- 5 stars77,26 %
- 4 stars16,58 %
- 3 stars5,12 %
- 2 stars0,51 %
- 1 star0,51 %
Principales reseñas sobre A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)
Matha and Adam, thank you again. I will try to apply what I learned here to my own work, a content recommendation system based on deep learning and reinforcement learning.
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.
The comments given by the auto grader is not informative of the errors causing problem, and not sensitive enough to capture problems with action selection steps based on current state.
Excellent specialization course with step by step capstone project. This specialization gave me confidence and a way to understand, learn and explore more on RL topics.
Acerca de Programa especializado: Aprendizaje por refuerzo
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si me suscribo a este Programa especializado?
¿Hay ayuda económica disponible?
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