A nested family of $$\varvec{}$$-total effective rewards for positional games

We consider Gillette’s two-person zero-sum stochastic games with perfect information. For each \(k \in \mathbb {N}=\{0,1,\ldots \}\) we introduce an effective reward function, called k-total. For \(k = 0\) and 1 this function is known as mean payoff and total reward, respectively. We restrict our attention to the deterministic case. For all k, we prove the existence of a saddle point which can be realized by uniformly optimal pure stationary strategies. We also demonstrate that k-total reward games can be embedded into \((k+1)\)-total reward games.