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Reinforcement Learning I (Intro Lecture)

Difficulty level

This lecture provides an introduction to a variety of topics in reinforcement learning.

Topics covered in this lesson
  • An overview of the features of a Reinforcement Learning system and general methods for predicting state values, including Monte Carlo methods, Dynamic Programming, and Temporal Differences learning 
  • An overview of the control problem in Reinforcement Learning, including the Bellman optimality equation, algorithms such as value iteration, Q-learning, SARSA, and methods for exploration
  • A brief introduction to function approximation and deep RL
  • Policy-based methods such as policy iteration, the actor-critic architecture, and the pros and cons of value-based vs. policy-based methods

Experience with Python Programming Language

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