Reinforcement Learning (Agent and environment). image source: Unity's blog on Unity Machine Learning Agents Toolkit

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To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning techniques with reinforcement learning. In addition, students will advance their understanding and the field of RL through a final project.

There is no official textbook for the class but a number of the supporting readings will come from:

• Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition. This is available for free here and references will refer to the final pdf version available here.

Some other additional references that may be useful are listed below:

• Reinforcement Learning: State-of-the-Art, Marco Wiering and Martijn van Otterlo, Eds. [link]
• Artificial Intelligence: A Modern Approach, Stuart J. Russell and Peter Norvig. [link]
• Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. [link]
• David Silver's course on Reinforcement Learning. [link]

Lecture notes & slides could be found [here].