Karol Hausman
The course website will be updated with Fall 2021 details in the coming days.
Humans, animals, and robots faced with the world must make decisions and take actions in the world. Moreover, the decisions they choose affect the world they exist in and those outcomes must be taken into account. This course is about algorithms for deep reinforcement learning – methods for learning behavior from experience, with a focus on practical algorithms that use deep neural networks to learn behavior from high-dimensional observations. Topics will include methods for learning from demonstrations, bo
The course will consist of twice weekly lectures, four homework assignments, and a final project. The lectures will cover fundamental topics in deep reinforcement learning, with a focus on methods that are applicable to domains such as robotics and control. The assignments will focus on conceptual questions and coding problems that emphasize these fundamentals. Finally, students will present their projects at a poster session and through a final report at the end of the quarter.
Karol Hausman