Comprising 10 lectures, it covers fundamentals, such as learning and planning in sequential decision problems, before progressing to more advanced topics and modern deep RL algorithms. This series will give students a detailed understanding of topics, including Markov Decision Processes, sample-based learning algorithms (e.g. Q-learning, SARSA), deep reinforcement learning, model-based reinforcement learning and planning (including Dyna), policy gradient algorithms and actor-critic methods. It also explores more advanced topics such as multi-step updates, double Q-learning and recent algroithms such as rainbow DQN.
The course is concluded by two guest lectures led by DeepMind Research Scientists Volodymyr Mnih and David Silver. Students might also enjoy the Deep Learning lecture series or the Coursera Specialisation on Reinforcment Learning taught by University of Alberta's Martha White and her colleague and DeepMind Research Scientist Adam White.
Suggested further reading: Reinforcement Learning: An introduction by Sutton and Barto.