Deep Reinforcement Learning Course

Welcome to the Deep Reinforcement Learning (DRL) course page! This course covers the fundamentals of DRL, including theory, algorithms, and practical applications.

Course Outline

• Introduction to DRL

  • What is DRL?
  • History and development
  • Key concepts and terminology

• Reinforcement Learning Basics

  • Markov Decision Processes (MDPs)
  • Value functions and policies
  • Q-learning and policy gradient methods

• Deep Learning for DRL

  • Deep Neural Networks (DNNs)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

• Advanced DRL Techniques

  • Asynchronous Advantage Actor-Critic (A3C)
  • Proximal Policy Optimization (PPO)
  • Deep Q-Network (DQN)

• Practical Applications

  • Autonomous driving
  • Robotics
  • Game playing

Course Resources

• Lecture Notes
• Assignments and Exercises
• Project Ideas

Related Links

• Reinforcement Learning: An Introduction
• Deep Learning for Reinforcement Learning

Deep Reinforcement Learning