The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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.
What's inside
Four courses
Fundamentals of Reinforcement Learning
(0 hours)
Fundamentals of Reinforcement Learning
(0 hours)
Sample-based Learning Methods
(0 hours)
Sample-based Learning Methods
(0 hours)
Prediction and Control with Function Approximation
(0 hours)
Prediction and Control with Function Approximation
(0 hours)
A Complete Reinforcement Learning System (Capstone)
(0 hours)
A Complete Reinforcement Learning System (Capstone)
(0 hours)Learning objectives
- Build a reinforcement learning system for sequential decision making.
- Understand the space of rl algorithms (temporal- difference learning, monte carlo, sarsa, q-learning, policy gradients, dyna, and more).
- Understand how to formalize your task as a reinforcement learning problem, and how to begin implementing a solution.
- Understand how rl fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning
- Build a reinforcement learning system for sequential decision making.
- Understand the space of rl algorithms (temporal- difference learning, monte carlo, sarsa, q-learning, policy gradients, dyna, and more).
- Understand how to formalize your task as a reinforcement learning problem, and how to begin implementing a solution.
- Understand how rl fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning
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