CS 294 Deep Reinforcement Learning, Spring 2017

Table of Contents


Prerequisites

This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning. Students who are not familiar with the concepts below are encouraged to brush up using the references provided right below this list. We’ll review this material in class, but it will be rather cursory.

  • Reinforcement learning and MDPs
    • Definition of MDPs
    • Exact algorithms: policy and value iteration
    • Search algorithms
  • Numerical Optimization
    • gradient descent, stochastic gradient descent
    • backpropagation algorithm
  • Machine Learning
    • Classification and regression problems: what loss functions are used, how to fit linear and nonlinear models
    • Training/test error, overfitting.

For introductory material on RL and MDPs, see

For introductory material on machine learning and neural networks, see

Source: CS 294 Deep Reinforcement Learning, Spring 2017