Covers: theory of Markov Decision Process
Estimated time needed to finish: 5 minutes
Questions this item addresses:
  • What is MDP?
How to use this item?

Read the "definition" and "Extensions and generalizations" sections

Author(s) / creator(s) / reference(s)
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Reinforcement Learning in the Real World

Total time needed: ~8 hours
This recipe provides a high level procedure for identifying and leveraging reinforcement learning related use cases in real world.
Potential Use Cases
stock trading, event scheduling
Who is This For ?
INTERMEDIATEanyone with high level understanding of machine learning and interested in potential RL use cases in industry
Click on each of the following annotated items to see details.
PDF 1. Matt Taylor- RL Masterclass - Session I slides
  • What is RL?
  • Where can it be used?
20 minutes
PAPER 2. An empirical investigation of the challenges of real-world reinforcement learning
  • What challenged does using RL in real world face?
10 minutes
CALL_TO_ACTION 3. Example RL agents learning in the browser
10 minutes
VIDEO 4. Deep RL Class Video Playlist
  • How does deep reinforcement learning work?
180 minutes
  • Where can i find everything about reinforcement learning?
15 minutes
OTHER 6. Udacity RL Class
  • What is reinforcement learning?
10 minutes
ARTICLE 7. Spinning Up in Deep RL
  • How can I quickly start implementing deep RL?
10 minutes
OTHER 8. Algorithms in Reinforcement Learning
  • What algorithms are used in reinforcement learning?
  • Why might you pick one algorithm over another?
10 minutes
ARTICLE 9. Markov Decision Process
  • What is MDP?
5 minutes
ARTICLE 10. Game Theory
  • What is game theory?
  • How does game theory relate to RL?
5 minutes
OTHER 11. Coursera RL specialization from U Alberta
  • What is reinforcement learning?
  • How can I use reinforcement learning?
180 minutes
ARTICLE 12. Using Dynamic Programming to find the optimal policy in Grid World
  • What is value iteration?
  • What is policy iteration?
10 minutes

Concepts Covered

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