scholarly journals A class of stochastic games with ordered field property

1990 ◽  
Vol 65 (3) ◽  
pp. 519-529 ◽  
Author(s):  
O. J. Vrieze ◽  
S. H. Tijs ◽  
T. Parthasarathy ◽  
C. A. J. M. Dirven
Keyword(s):  
Stochastic Games ◽  
Ordered Field ◽  
Ordered Field Property ◽  
Field Property

ORDERED FIELD PROPERTY IN A SUBCLASS OF FINITE SER-SIT SEMI-MARKOV GAMES

2013 ◽  
Vol 15 (04) ◽  
pp. 1340026 ◽  
Author(s):  
PRASENJIT MONDAL ◽  
SAGNIK SINHA
Keyword(s):  
Stochastic Game ◽  
Transition Probability ◽  
Pollution Tax ◽  
Markov Games ◽  
Ordered Field ◽  
Markov Game ◽  
Ordered Field Property ◽  
Single Matrix ◽  
Zero Sum ◽  
Field Property

In this paper, we deal with a subclass of two-person finite SeR-SIT (Separable Reward-State Independent Transition) semi-Markov games which can be solved by solving a single matrix/bimatrix game under discounted as well as limiting average (undiscounted) payoff criteria. A SeR-SIT semi-Markov game does not satisfy the so-called (Archimedean) ordered field property in general. Besides, the ordered field property does not hold even for a SeR-SIT-PT (Separable Reward-State-Independent Transition Probability and Time) semi-Markov game, which is a natural version of a SeR-SIT stochastic (Markov) game. However by using an additional condition, we have shown that a subclass of finite SeR-SIT-PT semi-Markov games have the ordered field property for both discounted and undiscounted semi-Markov games with both players having state-independent stationary optimals. The ordered field property also holds for the nonzero-sum case under the same assumptions. We find a relation between the values of the discounted and the undiscounted zero-sum semi-Markov games for this modified subclass. We propose a more realistic pollution tax model for this subclass of SeR-SIT semi-Markov games than pollution tax model for SeR-SIT stochastic game. Finite step algorithms are given for the discounted and for the zero-sum undiscounted cases.

Ordered Field Property for Semi-Markov Games when One Player Controls Transition Probabilities and Transition Times

2015 ◽  
Vol 17 (02) ◽  
pp. 1540022 ◽  
Author(s):  
Prasenjit Mondal ◽  
Sagnik Sinha
Keyword(s):  
Linear Programming ◽  
Transition Probabilities ◽  
Markov Games ◽  
Markov Strategy ◽  
Ordered Field ◽  
Player Game ◽  
Ordered Field Property ◽  
Transition Times ◽  
Zero Sum ◽  
Field Property

Two-person finite semi-Markov games (SMGs) are studied when the transition probabilities and the transition times are controlled by one player at all states. For the discounted games in this class, we prove that the ordered field property holds and there exist optimal/Nash equilibrium stationary strategies for the players. We illustrate that the zero-sum SMGs where only transition probabilities are controlled by one player, do not necessarily satisfy the ordered field property. An algorithm along with a numerical example for the discounted one player control zero-sum SMGs is given via linear programming. For the undiscounted version of such games, we exhibit with an example that if the game ceases to be unichain, an optimal stationary or Markov strategy need not exist, (though in this example of a one-player game we exhibit a semi-stationary optimal strategy/policy). Lastly, we prove that if such games are unichain, then they possess the ordered field property for the undiscounted case as well.

DISCOUNTING AND AVERAGING IN GAMES ACROSS TIME SCALES

2012 ◽  
Vol 23 (03) ◽  
pp. 609-625 ◽  
Author(s):  
KRISHNENDU CHATTERJEE ◽  
RUPAK MAJUMDAR
Keyword(s):  
Time Scales ◽  
Stochastic Game ◽  
Optimal Strategies ◽  
Sequential Decision ◽  
Ordered Field ◽  
Markov Decision ◽  
Mean Payoff Games ◽  
Upper Level ◽  
Ordered Field Property ◽  
Mean Payoff

We introduce two-level discounted and mean-payoff games played by two players on a perfect-information stochastic game graph. The upper level game is a discounted or mean-payoff game and the lower level game is a (undiscounted) reachability game. Two-level games model hierarchical and sequential decision making under uncertainty across different time scales. For both discounted and mean-payoff two-level games, we show the existence of pure memoryless optimal strategies for both players and an ordered field property. We show that if there is only one player (Markov decision processes), then the values can be computed in polynomial time. It follows that whether the value of a player is equal to a given rational constant in two-level discounted or mean-payoff games can be decided in NP ∩ coNP . We also give an alternate strategy improvement algorithm to compute the value.

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