A policy iteration algorithm for nonzero-sum stochastic impulse games
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This work presents a novel policy iteration algorithm to tackle nonzero-sum stochastic impulse games arising naturally in many applications. Despite the obvious impact of solving such problems, there are no suitable numerical methods available, to the best of our knowledge. Our method relies on the recently introduced characterisation of the value functions and Nash equilibrium via a system of quasi-variational inequalities. While our algorithm is heuristic and we do not provide a convergence analysis, numerical tests show that it performs convincingly in a wide range of situations, including the only analytically solvable example available in the literature at the time of writing.
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2020 ◽
Vol 50
(11)
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pp. 3972-3985
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2016 ◽
Vol 10
(12)
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pp. 1431-1439
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2010 ◽
Vol 62
(2)
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pp. 185-204
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2020 ◽
Vol 373
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pp. 112544
The policy iteration algorithm for average reward Markov decision processes with general state space
1997 ◽
Vol 42
(12)
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pp. 1663-1680
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