Covers: theory of Model-Free Policy Evaluation
Estimated time needed to finish: 20 minutes
Questions this item addresses:
  • What is Monte-Carlo policy evaluation technique?
How to use this item?

Go through slides 14 to 30

Author(s) / creator(s) / reference(s)
Prof. Emma Brunskill
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Model-Free Policy Evaluation

Contributors
Total time needed: ~3 hours
Objectives
Basic understanding of model-free approaches for policy evaluation
Potential Use Cases
Strategic Games, Robotics
Who is This For ?
BEGINNERAnyone who is interested in learning the concepts and real world applications of Reinforcement Learning
Click on each of the following annotated items to see details.
ARTICLE 1. What are expected values, variance, and covariance?
  • What are expected values and how do these relate to the concept of covariance?
20 minutes
OTHER 2. Monte-Carlo (MC) Policy Evaluation
  • What is Monte-Carlo policy evaluation technique?
20 minutes
OTHER 3. Temporal Difference (TD) Policy Evaluation
  • What is Temporal Difference (TD) policy evaluation?
15 minutes
BOOK_CHAPTER 4. MDP, MC and TD sections from Reinforcement Learning book
  • What is Markov Decision Process (MDP)?
  • What is Monte-Carlo (MC) Learning?
  • What is Temporal Difference (TD) Learning?
30 minutes
VIDEO 5. Markov Decision Processes - Part 1
  • Definition of Markov Decision Processes?
  • What is Markov about MDPs?
  • What is V-value and Q-value?
30 minutes
VIDEO 6. Markov Decision Processes - Part 2
  • What are Bellman equations?
  • What is Value iteration?
  • What is Policy iteration?
30 minutes

Concepts Covered

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