We consider simple stochastic games $\mathcal G$ with energy-parity objectives, a combination of quantitative rewards with a qualitative parity condition. The Maximizer tries to avoid running out of energy while simultaneously satisfying a parity condition. We present an algorithm to approximate the value of a given configuration in 2-NEXPTIME. Moreover, $\varepsilon$-optimal strategies for either player require at most $O(2EXP(|{\mathcal G}|)\cdot\log(\frac{1}{\varepsilon}))$ memory modes.

翻译：我们考虑带有能量-奇偶目标的简单随机博弈$\mathcal G$，该目标将定量奖励与定性奇偶条件相结合。最大化者试图在满足奇偶条件的同时避免能量耗尽。我们提出了一种算法，可在2-NEXPTIME时间内逼近给定配置的值。此外，任一玩家的$\varepsilon$-最优策略最多需要$O(2EXP(|{\mathcal G}|)\cdot\log(\frac{1}{\varepsilon}))$个记忆模式。