Reinforcement Learning — Monte Carlo Methods and Temporal Difference Learning | Computer Science Online Tutorial

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Reinforcement Learning

A self-paced online tutorial session on monte carlo methods and temporal difference learning, part of the Reinforcement Learning track. Work through the topics below, then reach out any time if you'd like a live walkthrough.

Estimated session length: 9 hours Format: Online, self-paced

What this tutorial covers

  • 01Model-free learning
  • 02Model-free prediction
  • 03Monte Carlo methods
  • 04Monte Carlo Prediction
  • 05Monte Carlo Estimation of Action Values
  • 06Temporal-Difference Learning
  • 07TD Prediction
  • 08Advantages of TD Prediction Methods
  • 09Optimality of TD(0) - n-step Bootstrapping - n-step TD Prediction - n-step Sarsa
  • 10Model-free control
  • 11Monte Carlo Control
  • 12Monte Carlo Control without Exploring Starts
  • 13Off policy learning
  • 14Importance sampling
  • 15Off-policy Monte Carlo Control
  • 16Sarsa: On-policy TD Control
  • 17Q-learning: Off-policy TD control
  • 18T5 TD Prediction implementation
  • 19T6 Cliff walking implementation

How to use this session

Read through each topic in order — they build on one another. Keep a notes document open and try to restate each concept in your own words before moving to the next one; that single habit does more for retention than re-reading ever will.

If any topic above needs a deeper explanation, a worked example, or a live session with an instructor, get in touch using the details below and we'll set up a time that works for you.

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