Syllabus

  1. Review and Preliminaries

    1. Introduction and Course Overview

    2. Computer Vision Review

    3. Robotics Review

    4. Markov Decision Processes Review

    5. Deep Reinforcement Learning

  2. Alternatives to Solving Unknown MDPs

    1. Model Building

    2. Imitation Learning

    3. Inverse Reinforcement Learning

    4. Social Learning

    5. Self-supervision

    6. Exploration

    7. Hierarchies and Skills

    8. Multi-task Learning

  3. Case Studies

    1. Navigation

    2. Manipulation

  4. Perspectives

    1. Lessons from Cognitive Science and Psychology

    2. Modern Deep RL vs Classical Control