Introduction to Deep Reinforcement Learning
This course will cover the basics of deep reinforcement learning with a focus on core algorithms. We will look at standard reinforcement learning methods like Q-learning and policy gradients along with their neural/deep counterparts. We will also cover a number of practical implementation issues like reward scaling and how to deal with large or continuous action spaces.
- TensorFlow 2
- Basic deep learning/neural networks (CNNs, SGD, etc)
- Basic RL (MDPs, value functions, TD, etc).
Level of complexity of course
Dr. Volodymyr Mnih
Research Scientist at DeepMind
Volodymyr is a Research Scientist at DeepMind where he works primarily on deep reinforcement learning. Prior to joining DeepMind, Volodymyr received a Ph.D. from the University of Toronto working with Geoffrey Hinton and an MSc from the University of Alberta working with Csaba Szepesvari.
Fields of interests: Reinforcement Learning, Deep Learning
Contacts: [email protected]