https://github.com/oiricaud/markov-decision-process-ai
Implement decision process for Monte-Carlo, Value Iteration & Q-Learning
https://github.com/oiricaud/markov-decision-process-ai
ai decision markov process
Last synced: 2 months ago
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Implement decision process for Monte-Carlo, Value Iteration & Q-Learning
- Host: GitHub
- URL: https://github.com/oiricaud/markov-decision-process-ai
- Owner: oiricaud
- Created: 2017-04-21T22:52:00.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-10-13T01:14:01.000Z (over 4 years ago)
- Last Synced: 2025-01-17T18:04:09.894Z (4 months ago)
- Topics: ai, decision, markov, process
- Language: Java
- Homepage:
- Size: 160 KB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Markov-Decision-Process: Artificial Intelligence
Objective
======
To expirement with some of the basic algorithms for solving MDPs on a simple domain.
Groups: You may optionally work in groups of 2 students.
Doomain: The domain is based on a simple MDP originally designed by Rich Sutton at the University of Alberta. The example describes a Markov Decision Porcess that models the life of a student and the decisions one must make to both have a good time and remain in good academic standing.
States
======
R = Rested
T = Tired
D = Homework Done
U = Homework Undone
8p = eight o'clock pm
Actions
======
P = Party
R = Rest
S = Study
any means any action has the same effect
*note: not all actions are possible in all states*
Red numbers are rewards
Green numbers are transition probabilities (all those not labeled are probability 1.0)
The gray rectangle denotes a terminal state.
See below for the diagram of the MDP.
