A Complete Reinforcement Learning System (Capstone)

You will use your expertise from Courses 1, 2, and 3 to construct a comprehensive RL solution to a problem in this last course. This capstone will show you how each component—-problem formulation, algorithm selection, parameter selection, and representation design—-combines to build a full solution, as well as how to make proper decisions while using RL in the real world. This project will need you to create both a stimulating environment and a control agent that approximates Neural Network functions. You will also perform a scientific analysis of your learning system in order to improve your capacity to evaluate the robustness of RL agents.

To utilize RL in the real world, you must properly formulate the issue as an MDP, pick appropriate algorithms, determine which implementation decisions will have a significant influence on performance, and evaluate your algorithms' predicted behavior. This capstone is essential for anybody who intends to use RL to tackle real-world challenges. You must have completed or equivalent to Courses 1, 2, and 3 in this Specialization to be successful in this course.

You will be able to do the following by the conclusion of this course: Complete an RL solution to an issue, beginning with the formulation of the problem, proper algorithm selection and implementation, and an empirical examination of the solution's efficacy.

• Flexible deadlines: Reset deadlines based on your availability.
• Shareable certificate: Get a Certificate when you complete
• 100% online: Start now and learn at times that suit you.
• Course 4 of 4 in the: Reinforcement Learning Specialization
• Intermediate level: Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
• Approx. 4 p.m. to complete
• Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

SKILLS YOU WILL GAIN

• Artificial Intelligence (AI)
• Machine-learning
• Reinforcement Learning
• Function Approximation
• Intelligent Systems

Rating: 4.7/5

Enroll here: coursera.org/learn/complete-reinforcement-learning-system