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September 25, 2023


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CS234: Reinforcement Learning. Instructor: Prof. Emma Brunskill, Department of Computer Science, Stanford University.

To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning ? an extremely promising new area that combines deep learning techniques with reinforcement learning. In addition, students will advance their understanding and the field of RL through a final project. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Course Curriculum

  • Lecture 01 – Introduction Unlimited
  • Lecture 02 – Given a Model of the World Unlimited
  • Lecture 03 – Model-Free Policy Evaluation Unlimited
  • Lecture 04 – Model Free Control Unlimited
  • Lecture 05 – Value Function Approximation Unlimited
  • Lecture 06 – CNNs and Deep Q Learning Unlimited
  • Lecture 07 – Imitation Learning Unlimited
  • Lecture 08 – Policy Gradient I Unlimited
  • Lecture 09 – Policy Gradient II Unlimited
  • Lecture 10 – Policy Gradient III and Review Unlimited
  • Lecture 11 – Fast Reinforcement Learning Unlimited
  • Lecture 12 – Fast Reinforcement Learning II Unlimited
  • Lecture 13 – Fast Reinforcement Learning III Unlimited
  • Lecture 15 – Batch Reinforcement Learning Unlimited
  • Lecture 16 – Monte Carlo Tree Search Unlimited

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