Contributors

Covers: theory of Model-Free Policy Evaluation

- What is Temporal Difference (TD) policy evaluation?

Go through slides 30 to 37

Prof. Emma Brunskill

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Contributors

- 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.

Resources

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

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