Lecture Series

  • Lecture 1: Introduction to Reinforcement Learning (RL)

    Date & time: 16.08.2021

    Slides: PDF

    This lecture will focus on fundamental concepts and challenges of RL:

    • Basics of RL
    • Exact solution methods for planning
    • Value-based vs policy-based methods
    • Modern challenges and open questions

  • Lecture 2: RL from Control Perspectives

    Date & time: 17.08.2021

    Slides: PDF

    This lecture will focus on the convergence analysis of valued-based RL from unified dynamical systems perspectives :

    • TD learning
    • Q-learning
    • Double Q-learning
    • Variants w/o function approximation

  • Lecture 3: RL from Optimization Perspectives

    Date & time: 18.08.2021

    Slides: PDF

    This lecture will focus on the global convergence analysis of policy-based RL from nonconvex optimization perspectives:

    • Policy gradient method
    • Natural policy gradient method
    • Policy gradient methods with function approximation
    • Policy gradient methods with entropy regularization

  • Lecture 4: RL from Deep Learning Perspectives

    Date & time: 19.08.2021

    Slides: PDF

    This lecture will focus on the generalization and regularization of deep RL from deep learning perspectives:

    • A refresher of modern deep learning theory
    • TD learning with neural network approximation
    • Actor-critic with neural network approximation

  • Lecture 5: RL from Game Perspectives

    Date & time: 20.08.2021

    Slides: PDF

    This lecture will focus on game perspetives of RL :

    • Zero-sum game (from convex-concave to nonconvex-nonconcave)
    • Stackelberg game