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Reinforcement Learning Lecture Series 2021

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.

https://deepmind.com/learning-resources/reinforcement-learning-series-2021

Reinforcement Learning

Reinforcement learning is a body of theory and algorithms for optimal decision making developed within the machine learning and operations research communities in the last twenty-five years, and which have separately become important in psychology and neuroscience. Reinforcement learning methods find useful approximate solutions to optimal-control problems that are too large or too ill-defined for classical methods such as dynamic programming. For example, reinforcement-learning methods have obtained the best-known solutions in such diverse automation applications as helicopter flying, elevator scheduling, playing backgammon, and resource-constrained scheduling.