CS234 - Reinforcement Learning

Course Description

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 an open ended project.

Learning Outcomes

• Define the key features of reinforcement learning that distinguishes it from AI and non-interactive machine learning (as assessed by the exam).
• Given an application problem (e.g. from computer vision, robotics, etc), decide if it should be formulated as a RL problem; if yes be able to define it formally (in terms of the state space, action space, dynamics and reward model), state what algorithm (from class) is best suited for addressing it and justify your answer (as assessed by the project and the exam).
• Implement in code common RL algorithms such as a deep RL algorithm, including imitation learning (as assessed by the homeworks).
• Describe (list and define) multiple criteria for analyzing RL algorithms and evaluate algorithms on these metrics: e.g. regret, sample complexity, computational complexity, empirical performance, convergence, etc (as assessed by homeworks and the exam).
• Describe the exploration vs exploitation challenge and compare and contrast at least two approaches for addressing this challenge (in terms of performance, scalability, complexity of implementation, and theoretical guarantees) (as assessed by an assignment and the exam).

Textbooks

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 January 1 2018 draft 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]