![Review: Markov Decision Processes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Cs221 rl
- 1. CS 221: Artificial Intelligence Reinforcement Learning Peter Norvig and Sebastian Thrun Slide credit: Dan Klein, Stuart Russell, Andrew Moore
- 9. Example +1 -1 0 0 0 0 0 0 0 0 0
- 10. Example +1 -1 0 0 0 0 0 0 0 0 0
- 11. Example +1 -1 0 0 0 0 0 0 0.9 0 0
- 12. Example +1 -1 0 0 0 0 0 0 0.9 0 0
- 13. Example +1 -1 0 0 0 0 0 0.8 0.92 0 0
- 14. Example +1 -1 -0.01 -.16 .12 -.12 .17 .28 .36 .20 -.2
- 15. Sample results
- 22. Q-Learning 0 0 0 +1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 23. Q-Learning 0 0 0 +1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 24. Q-Learning 0 0 0 +1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ?? 0 0 0 0 0 0 0 0 0 0 0 0
- 25. Q-Learning 0 0 0 +1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .45 0 0 0 0 0 0 0 0 0 0 0 0
- 26. Q-Learning 0 0 0 +1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .33 .78 0 0 0 0 0 0 0 0 0 0 0 0