Mastering Advanced Reinforcement Learning Concepts

Description

Course Curriculum

Module: 1 – Introduction to Reinforcement Learning (Stanford CS234 Winter 2019 Lecture 1)

  • Introduction to Reinforcement Learning (Stanford CS234 Winter 2019 Lecture 1)
    00:00

Module: 2 – Learning with a Known Model of the Environment (Lecture 2)

Module: 3 – Model-Free Policy Evaluation Methods (Lecture 3)

Module: 4 – Model-Free Control Techniques (Lecture 4)

Module: 5 – Function Approximation for Value Estimation (Lecture 5)

Module: 6 – Convolutional Networks and Deep Q-Learning (Lecture 6)

Module: 7 – Imitation Learning Fundamentals (Lecture 7)

Module: 8 – Policy Gradient Methods I (Lecture 8)

Module: 9 – Advanced Policy Gradient Methods II (Lecture 9)

Module: 10 – Policy Gradient Methods III and Course Review (Lecture 10)

Module: 11 – Efficient / Fast Reinforcement Learning Approaches I (Lecture 11)

Module: 12 – Efficient / Fast Reinforcement Learning Approaches II (Lecture 12)

Module: 13 – Efficient / Fast Reinforcement Learning Approaches III (Lecture 13)

Module: 14 – Batch Reinforcement Learning Techniques (Lecture 15)

Module: 15 – Monte Carlo Tree Search Methods (Lecture 16)

$14.00 $114.00
  • Level
    Intermediate
  • Last Updated
    June 2, 2026