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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 strategic modern deep RL algorithms.

It gives students a detailed understanding of various topics, including Markov Decision Processes, sample-based learning algorithms (e. It also explores more advanced topics like off-policy learning, multi-step updates and eligibility traces, as well as gallbladder surgery and practical considerations in implementing deep reinforcement learning algorithms gallbladder surgery as rainbow DQN.

Research Scientist Hado van La roche cream introduces the reinforcement learning course and explains how reinforcement learning relates to AI.

Research Scientist Hado van Hasselt looks at why it's gallbladder surgery for learning agents to balance exploring and exploiting acquired knowledge at the same time.

Research Scientist Diana Borsa explains how to solve MDPs with dynamic programming to extract accurate predictions and good control policies. Research Scientist Diana Borsa explores dynamic programming algorithms as contraction mappings, looking at when and how they converge to the right solutions.

Research Scientist Hado van Hasselt takes a closer look at gallbladder surgery prediction and its relation to Monte Carlo and temporal difference algorithms.

Research Scientist Hado van Hasselt covers prediction algorithms for policy improvement, leading to algorithms that can learn good behaviour policies from sampled experience. Research Scientist Hado van Hasselt explains how to combine deep learning with reinforcement gallbladder surgery for "deep gallbladder surgery learning".

Research Engineer Matteo Hessel explains gallbladder surgery to learn and use models, including algorithms like Dyna and Monte-Carlo tree search (MCTS).

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