This presentation about Artificial intelligence Reinforcement learning in the grid world problem

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Reinforcement Learning
in The Grid World problem
Author
Alireza Andalib
Learning Machine

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Supervised Learning:
Example Class
Reinforcement Learning:
Situation Reward Situation Reward
…
RL

7. Supervised Learning
SystemInputs Outputs
Training Info = desired (target) outputs
Error = (target output – actual output)
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8. RL
SystemInputs Outputs (“actions”)
Training Info = evaluations (“rewards” / “penalties”)
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10. 10}|Pr{),( ssaaas ttt ===π

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Markov Decision Processes

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Grid World
A B
B’
A’

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21. Bellman

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2525
1.7120 9.7461 3.1311 5.4209 1.0036
0.7994 2.9233 2.3299 1.9586 0.4665
0.0023 0.7899 07355 0.4364 0.2287-
0.7664- 0.8488- 0.0076 0.1855- 0.9621-
0.9949- 1.3554- 1.0946- 1.4766- 2.0021-

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IPE

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IPE
K100i,j
1.4008 9.5698 3.1841 5.4309 0.8827
0.6503 2.9231 1.9576 1.8581 0.3910
0.0303- 0.8137 0.7354 0.4787 0.2830-
0.4062- 0.0118- 0.0183 0.1828- 0.7333-
0.6535- 0.4780- 0.4594- 0.5763- 0.9488-

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PI

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PI
State
Go Right Jump Go Left Jump Go Left
Go Up Go Up Go Left Go Up Go Left
Go Up Go Up Go Up Go Up Go Left
Go Up Go Up Go Up Go Up Go Left
Go Up Go Up Go Up Go Up Go Left

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 Horstmann, Cay. "GridWorld". horstmann.com.
Accessed September 15, 2008
 www.inf.ed.ac.uk/teaching/courses/rl
 www.math-info.univ-paris5.fr/~bouzy/Doc/AA2/Reinforcement
 www.cs.berkeley.edu/~pabbeel/cs287-fa12
 courses.cs.washington.edu/courses/cse473/12sp/s
lides/16-mdp.pdf

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