Portrait of Research Scientist Hado van Hasselt
Learning resources

Reinforcement Learning Lecture Series 2018

Lecture 1: Introduction to Reinforcement Learning
Hado shares an introduction to reinforcement learning, including an overview of core concepts and agent components.
Lecture 2: Exploration and Exploitation
Discusses the trade-off between exploration and exploitation and introduces key concepts such as multi-armed bandits.
Lecture 3: Markov Decision Processes and Dynamic Programming
Explores the theory of how agents interact with their environment, known as Markov Decision Processses (MDP).
Lecture 4: Model-Free Prediction and Control
A deep dive into how model-free prediction and control can be used to estimate and optimise values in MDPs.
Lecture 5: Function Approximation and Deep Reinforcement Learning
A deep dive into how agents can use function approximation to determine a policy, value function or model.
Lecture 6: Policy Gradients and Actor Critics
Examines policy based RL and how an agency can learn a policy directly from experience.
Lecture 7: Planning and Models
Explores how agents can learn a model directly from experience and then use it to plan and construct a value function or policy.
Lecture 8: Advanced Topics in Deep RL
An overview of open research questions including exploration, credit assignment and sample efficient learning.
Lecture 9: A Brief Tour of Deep RL Agents
Research Scientist Volodymyr Mnih, one of the team behind DQN, gives an overview of deep reinforcement learning agents.
Lecture 10: Classic Games Case Study
David Silver, the co-creator of AlphaZero and AlphaStar, gives an overview of RL and its application to classic games.