A policy iteration algorithm for nonzero-sum stochastic impulse games
For researchers and practitioners dealing with nonzero-sum stochastic impulse games, this provides the first numerical method, though it is heuristic without convergence guarantees.
This work presents a novel policy iteration algorithm for nonzero-sum stochastic impulse games, which previously lacked suitable numerical methods. Numerical tests show convincing performance across a wide range of situations, including the only analytically solvable example.
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 characterization 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.