Reinforcement Learning

By the end of this Specialization, learners will understand the foundations of much of modern probabilistic AI and be prepared to take more advanced courses, or to apply AI tools and ideas to real-world problems.

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Gain new skills and advance your career with this Reinforcement Learning specialization from University of Alberta.

Reinforcement Learning Specialization description

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).

Harnessing the full potential of AI requires adaptive learning systems; this is exactly what reinforcement learning (RL) does by design: improve through trial-and-error interaction.

By the end of this Specialization, learners will understand the foundations of much of modern probabilistic 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.

The tools learned in this Specialization can be applied to:

  • AI in game development,
  • IOT devices,
  • Clinical decision making,
  • Industrial process control,
  • Finance portfolio balancing,
  • & more.

Learning Outcomes

  • Build a RL system that knows how to make automated decisions
  • Understand how RL relates and fits into 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

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Reinforcement Learning
Reinforcement Learning
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