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A Potential Reduction Algorithm for Two-Person Zero-Sum Mean Payoff Stochastic Games

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We suggest a new algorithm for two-person zero-sum undiscounted stochastic games focusing on stationary strategies. Given a positive real $$\varepsilon $$ε, let us call a stochastic game $$\varepsilon $$ε-ergodic, if… Click to show full abstract

We suggest a new algorithm for two-person zero-sum undiscounted stochastic games focusing on stationary strategies. Given a positive real $$\varepsilon $$ε, let us call a stochastic game $$\varepsilon $$ε-ergodic, if its values from any two initial positions differ by at most $$\varepsilon $$ε. The proposed new algorithm outputs for every $$\varepsilon >0$$ε>0 in finite time either a pair of stationary strategies for the two players guaranteeing that the values from any initial positions are within an $$\varepsilon $$ε-range, or identifies two initial positions u and v and corresponding stationary strategies for the players proving that the game values starting from u and v are at least $$\varepsilon /24$$ε/24 apart. In particular, the above result shows that if a stochastic game is $$\varepsilon $$ε-ergodic, then there are stationary strategies for the players proving $$24\varepsilon $$24ε-ergodicity. This result strengthens and provides a constructive version of an existential result by Vrieze (Stochastic games with finite state and action spaces. PhD thesis, Centrum voor Wiskunde en Informatica, Amsterdam, 1980) claiming that if a stochastic game is 0-ergodic, then there are $$\varepsilon $$ε-optimal stationary strategies for every $$\varepsilon > 0$$ε>0. The suggested algorithm is based on a potential transformation technique that changes the range of local values at all positions without changing the normal form of the game.

Keywords: game; varepsilon; stochastic games; stationary strategies; algorithm two

Journal Title: Dynamic Games and Applications
Year Published: 2018

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