What are the possible policies in this MDP? 5. Markov Decision ProcessConsider the following scenario. You are reading email, and you get an offer from the CEO of MarsomaniaLtd., asking you to consider investing into an expedition, which plans to dig for gold on Mars. You can eitherchoose to invest, with the prospect of either getting money or fooled, or you can instead choose to ignoreyour emails and go to a party. Of course your first thought is to model this as Markov Decision Process, andyou come up with the MDP as follows.Get money StayInvestReadEmails (E)R=0Go toparty(M)R=10000.2Be fooled(F)Re-1001 Go backStayHave fun(H)R-1 Your MDP has four states: Read emails (E), Get money (M), Be fooled (F) or Have fun (H). The actions aredenoted by fat arrows, the (probabilistic) transitions are indicated by thin arrows, annotated by the transitionprobabilities. The rewards only depend on the state, for example, the reward in state E is 0, in state M it is10,000.

Answer: There are 2 policies: both map state M to Stay, F to Go back and H to Stay. The only…

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.

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter17: Markov Chains

Section17.4: Classification Of States In A Markov Chain

Problem 5P

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