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Long-term values in Markov Decision Processes and Repeated Games, and a new distance for probability spaces

Author

Listed:
  • Jérôme Renault

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Xavier Venel

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

We study long-term Markov Decision Processes and Gambling Houses, with applications to any partial observation MDPs with finitely many states and zero-sum repeated games with an informed controller. We consider a decision-maker which is maximizing the weighted sum t≥1 θtrt, where rt is the expected reward of the t-th stage. We prove the existence of a very strong notion of long-term value called general uniform value, representing the fact that the decision-maker can play well independently of the evaluations (θt) t≥1 over stages, provided the total variation (or impatience) t≥1 |θt+1 − θt| is small enough. This result generalizes previous results of Rosenberg, Solan and Vieille [35] and Renault [31] that focus on arithmetic means and discounted evaluations. Moreover, we give a variational characterization of the general uniform value via the introduction of appropriate invariant measures for the decision problems, generalizing the fundamental theorem of gambling or the Aumann-Maschler cavu formula for repeated games with incomplete information. Apart the introduction of appropriate invariant measures, the main innovation in our proofs is the introduction of a new metric d * such that partial observation MDP's and repeated games with an informed controller may be associated to auxiliary problems that are non-expansive with respect to d *. Given two Borel probabilities over a compact subset X of a normed vector space, we define d * (u, v) = sup f ∈D 1 |u(f) − v(f)|, where D1 is the set of functions satisfying: ∀x, y ∈ X, ∀a, b ≥ 0, af (x) − bf (y) ≤ ax − by. The particular case where X is a simplex endowed with the L 1-norm is particularly interesting: d * is the largest distance over the probabilities with finite support over X which makes every disintegration non-expansive. Moreover, we obtain a Kantorovich-Rubinstein type duality formula for d * (u, v) involving couples of measures (α, β) over X × X such that the first marginal of α is u and the second marginal of β is v. MSC Classification: Primary: 90C40 ; Secondary: 60J20, 91A15.

Suggested Citation

  • Jérôme Renault & Xavier Venel, 2017. "Long-term values in Markov Decision Processes and Repeated Games, and a new distance for probability spaces," Post-Print hal-01396680, HAL.
  • Handle: RePEc:hal:journl:hal-01396680
    DOI: 10.1287/moor.2016.0814
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    References listed on IDEAS

    as
    1. Robert J. Aumann, 1995. "Repeated Games with Incomplete Information," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262011476, April.
    2. Dinah Rosenberg & Eilon Solan & Nicolas Vieille, 2000. "Blackwell Optimality in Markov Decision Processes with Partial Observation," Discussion Papers 1292, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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    8. VIEILLE, Nicolas & ROSENBERG, Dinah & SOLAN, Eilon, 2002. "Stochastic games with a single controller and incomplete information," HEC Research Papers Series 754, HEC Paris.
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    10. Jérôme Renault, 2012. "The Value of Repeated Games with an Informed Controller," Mathematics of Operations Research, INFORMS, vol. 37(1), pages 154-179, February.
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    Cited by:

    1. Fabien Gensbittel & Marcin Peski & Jérôme Renault, 2019. "The Large Space Of Information Structures," Working Papers hal-02075905, HAL.
    2. Li, Jin & Quincampoix, Marc & Renault, Jérôme & Buckdahn, Rainer, 2019. "Representation formulas for limit values of long run stochastic optimal controls," TSE Working Papers 19-1007, Toulouse School of Economics (TSE).
    3. Koessler, Frederic & Laclau, Marie & Renault, Jérôme & Tomala, Tristan, 2022. "Long information design," Theoretical Economics, Econometric Society, vol. 17(2), May.
    4. Frédéric Koessler & Marie Laclau & Jerôme Renault & Tristan Tomala, 2022. "Long information design," Post-Print hal-03700394, HAL.
    5. Frédéric Koessler & Marie Laclau & Jerôme Renault & Tristan Tomala, 2022. "Long information design," PSE-Ecole d'économie de Paris (Postprint) hal-03700394, HAL.
    6. Rida Laraki & Jérôme Renault, 2020. "Acyclic Gambling Games," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1237-1257, November.

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