Markov Decision Processes in Artificial Intelligence by Olivier Sigaud, Olivier Buffet

By Olivier Sigaud, Olivier Buffet

Markov selection tactics (MDPs) are a mathematical framework for modeling sequential choice difficulties below uncertainty in addition to Reinforcement studying difficulties. Written by means of specialists within the box, this e-book offers a world view of present study utilizing MDPs in synthetic Intelligence. It starts off with an introductory presentation of the elemental elements of MDPs (planning in MDPs, Reinforcement studying, partly Observable MDPs, Markov video games and using non-classical criteria). Then it provides extra complicated learn traits within the area and offers a few concrete examples utilizing illustrative applications.Content:
Chapter 1 Markov determination strategies (pages 1–38): Frederick Garcia and Emmanuel Rachelson
Chapter 2 Reinforcement studying (pages 39–66): Olivier Sigaud and Frederick Garcia
Chapter three Approximate Dynamic Programming (pages 67–98): Remi Munos
Chapter four Factored Markov selection approaches (pages 99–126): Thomas Degris and Olivier Sigaud
Chapter five Policy?Gradient Algorithms (pages 127–152): Olivier Buffet
Chapter 6 on-line answer concepts (pages 153–184): Laurent Peret and Frederick Garcia
Chapter 7 in part Observable Markov selection approaches (pages 185–228): Alain Dutech and Bruno Scherrer
Chapter eight Stochastic video games (pages 229–276): Andriy Burkov, Laetitia Matignon and Brahim Chaib?Draa
Chapter nine DEC?MDP/POMDP (pages 277–318): Aurelie Beynier, Francois Charpillet, Daniel Szer and Abdel?Illah Mouaddib
Chapter 10 Non?Standard standards (pages 319–360): Matthieu Boussard, Maroua Bouzid, Abdel?Illah Mouaddib, Regis Sabbadin and Paul Weng
Chapter eleven on-line studying for Micro?Object Manipulation (pages 361–374): Guillaume Laurent
Chapter 12 Conservation of Biodiversity (pages 375–394): Iadine Chades
Chapter thirteen self reliant Helicopter looking for a touchdown sector in an doubtful surroundings (pages 395–412): Patrick Fabiani and Florent Teichteil?Kunigsbuch
Chapter 14 source intake keep an eye on for an self sustaining robotic (pages 413–424): Simon Le Gloannec and Abdel?Illah Mouaddib
Chapter 15 Operations making plans (pages 425–452): Sylvie Thiebaux and Olivier Buffet

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Extra info for Markov Decision Processes in Artificial Intelligence

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4. More formally, let (s0 , s1 , . . , sN ) be a trajectory consistent with the policy π and the unknown transition function p(), and let (r1 , r2 , . . , rN ) be the rewards observed along this trajectory. In the Monte Carlo method, the N values V (st ), t = 0, . . 5) with the learning rates α(st ) converging to 0 along the iterations. Then V converges almost surely towards V π under very general assumptions [BER 96]. This method is called “every-visit” because the value of a state can be updated several times along the same trajectory.

Such an Markov Decision Processes 11 expression requires the decision epochs to be regularly dispatched in N. The main formal interest of the γ factor is to guarantee the convergence of the series used in the criterion and, thus, the existence of a value for the criterion in all cases. In practice, this discount factor is naturally introduced in the modeling process of economic 1 , where τ is the inflation rate’s value. 3. The total reward criterion However, we can still choose γ = 1 in some specific cases of infinite horizon problems.

This framework has become, in recent years, a premier methodological tool to model, analyze and solve problems of sequential decision under uncertainty in artificial intelligence. Despite its genericity, the theoretical framework presented in the previous pages also has its limitations, even in formal terms. First of all, it relies on the assumption that the agent has a perfect knowledge of the transition and reward models defining the problem at hand. We will see in Chapter 2 how reinforcement learning relaxes this hypothesis.

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