Dominated Strategy Elimination Based on the Regret Minimization Algorithm

Accelerating the convergence of algorithms to Nash equilibrium is an important issue in game theory. In many games, particularly in complex game scenarios, the strategy space is typically vast, containing numerous action combinations, with dominated strategies occupying a significant portion of the space. Based on this insight, this paper proposes a dominated strategy elimination method based on the Regret Minimization (RM) algorithm. The method effectively reduces the strategy space by eliminating dominated strategies during the iterative process. Experimental results demonstrate that the proposed strategy significantly reduces computational resource consumption and accelerates the algorithm's convergence to Nash equilibrium (NE).