Your MDP has four states: Read emails (E), Get money (M), Be fooled (F) or Have fun (H). The actions are denoted by fat arrows, the (probabilistic) transitions are indicated by thin arrows, annotated by the transition probabilities. The rewards only depend on the state, for example, the reward in state E is 0, in state M it is 10,000.
Your MDP has four states: Read emails (E), Get money (M), Be fooled (F) or Have fun (H). The actions are denoted by fat arrows, the (probabilistic) transitions are indicated by thin arrows, annotated by the transition probabilities. The rewards only depend on the state, for example, the reward in state E is 0, in state M it is 10,000.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
What are the possible policies in this MDP?

Transcribed Image Text:5. Markov Decision Process
Consider the following scenario. You are reading email, and you get an offer from the CEO of Marsomania
Ltd., asking you to consider investing into an expedition, which plans to dig for gold on Mars. You can either
choose to invest, with the prospect of either getting money or fooled, or you can instead choose to ignore
your emails and go to a party. Of course your first thought is to model this as Markov Decision Process, and
you come up with the MDP as follows.
Get money Stay
Invest
Read
Emails (E)
R=0
Go to
party
(M)
R=10000
.2
Be fooled
(F)
Re-100
1 Go back
Stay
Have fun
(H)
R-1

Transcribed Image Text:Your MDP has four states: Read emails (E), Get money (M), Be fooled (F) or Have fun (H). The actions are
denoted by fat arrows, the (probabilistic) transitions are indicated by thin arrows, annotated by the transition
probabilities. The rewards only depend on the state, for example, the reward in state E is 0, in state M it is
10,000.
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