scholarly journals Mean-payoff games with partial observation

2018 ◽  
Vol 735 ◽  
pp. 82-110 ◽  
Author(s):  
Paul Hunter ◽  
Arno Pauly ◽  
Guillermo A. Pérez ◽  
Jean-François Raskin
Author(s):  
Paul Hunter ◽  
Guillermo A. Pérez ◽  
Jean-François Raskin

2020 ◽  
Vol 53 (4) ◽  
pp. 390-396
Author(s):  
Yiding Ji ◽  
Xiang Yin ◽  
Wei Xiao

Automatica ◽  
2021 ◽  
Vol 123 ◽  
pp. 109359
Author(s):  
Yiding Ji ◽  
Xiang Yin ◽  
Stéphane Lafortune

2012 ◽  
Vol 23 (03) ◽  
pp. 585-608 ◽  
Author(s):  
LUBOŠ BRIM ◽  
JAKUB CHALOUPKA

We design a novel algorithm for solving Mean-Payoff Games (MPGs). Besides solving an MPG in the usual sense, our algorithm computes more information about the game, information that is important with respect to applications. The weights of the edges of an MPG can be thought of as a gained/consumed energy – depending on the sign. For each vertex, our algorithm computes the minimum amount of initial energy that is sufficient for player Max to ensure that in a play starting from the vertex, the energy level never goes below zero. Our algorithm is not the first algorithm that computes the minimum sufficient initial energies, but according to our experimental study it is the fastest algorithm that computes them. The reason is that it utilizes the strategy improvement technique which is very efficient in practice.


2007 ◽  
Vol 145 (3) ◽  
pp. 4967-4974 ◽  
Author(s):  
Y. M. Lifshits ◽  
D. S. Pavlov

2014 ◽  
Vol 24 (4) ◽  
pp. 2096-2117 ◽  
Author(s):  
Xavier Allamigeon ◽  
Pascal Benchimol ◽  
Stéphane Gaubert ◽  
Michael Joswig

1979 ◽  
Vol 8 (2) ◽  
pp. 109-113 ◽  
Author(s):  
A. Ehrenfeucht ◽  
J. Mycielski

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