Pulkit Agrawal, Jacob Huh, Anurag Ajay, Zhang-Wei Hong, Andi Peng, Aviv Netanyahu, Xiang Fu, Anthony Simeonov, Tao Chen, Avery Lamp

Deep Learning for Control

An overview of current deep reinforcement learning methods, challenges, and open research topics. The course will be taught by current members of the Improbable AI Lab at CSAIL, with the goal of providing a “bootcamp” for those wishing to get up to speed on current work in Robotics and Deep RL. Weeks 1-2 will detail understanding intelligence via machine learning to deep reinforcement architectures and frameworks (including methods for learning from demonstrations and practical RL). Week 3 will focus on learning for robotics and designing for efficient deep learning infrastructures.