Course Summary

Getting things wrong is part of what makes us human, and our natural intelligence helps us learn from our mistakes. Reinforcement learning is an area of machine learning which enables artificial intelligence to learn from its mistakes as well, for example allowing a robot to use trial-and-error to interact with a new environment and achieve an objective. This advanced course examines the fundamentals of reinforcement learning and explores the varied applications of dynamic programming methods.

The course will begin with a thorough grounding in the key theoretical concepts of reinforcement learning, familiarising you with agents, environments, and rewards, before introducing Markov decision processes, dynamic programming, and Monte Carlo methods. As the course progresses you will explore a wide range of reinforcement learning methods and techniques, including policy gradient methods and how they optimise policies, policy search methods such as evolutionary strategies and hill-climbing, and the cross-entropy method for policy optimisation. The final part of the course will introduce even more advanced topics, including multi-agent reinforcement learning.

This intensive course offers students theoretical understanding and practical experience in a range of reinforcement learning concepts and techniques, offering career skills as well as excellent foundations for future research.

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Key Features

  • Live and study in Lady Margaret Hall, one of Oxford's finest colleges
  • Learn from experienced academics using the tutorial system
  • Enjoy meals in hall, experiencing life as an Oxford student
  • Gain new skills to take you further in your future academic or professional career.

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