Deepmind
  

  
    

Reinforcement Learning Lecture Series 2021 19

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Taught by DeepMind researchers, this series was created in collaboration with University College London (UCL) to offer students a comprehensive introduction to modern reinforcement learning.
Comprising 13 lectures, the series covers the fundamentals of reinforcement learning and planning in sequential decision problems, before progressing to more advanced topics and modern deep RL algorithms. It gives students a detailed understanding of various topics, including Markov Decision Processes, sample-based learning algorithms (e.g. (double) Q-learning, SARSA), deep reinforcement learning, and more. It also explores more advanced topics like off-policy learning, multi-step updates and eligibility traces, as well as conceptual and practical considerations in implementing deep reinforcement learning algorithms such as rainbow DQN.

Previous series:
2020: The Deep Learning Lecture Series 2020 | DeepMind 1
2018: Reinforcement Learning Lecture Series 2018 | DeepMind
2018: Deep Learning Lecture Series 2018 | DeepMind