Reinforcement Learning Specialization
Programming Assignments
Disclaimer
You should only use the programming assignments placed in this repository as a resource and to get you out of a jam. Do not simply copy and paste the code if you are a participant in this course; doing so will not help you. Please watch every lecture video, finish the quizzes and code assignments yourself as it is comparatively simple to understand.
About
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.
By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science.
The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.
Applied Learning Project
Through programming assignments and quizzes, students will:
Build a Reinforcement Learning system that knows how to make automated decisions.
Understand how RL relates to and fits under the broader umbrella of machine learning, deep learning, supervised and unsupervised learning.
Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradient, Dyna, and more).
Understand how to formalize your task as a RL problem, and how to begin implementing a solution.