Bounding Average-energy Games

TL;DR

This paper investigates average-energy games with lower bounds, establishing their decidability, memory bounds, and computational complexity, and extends the analysis to multi-dimensional cases, identifying decidability boundaries.

Contribution

It provides a doubly-exponential upper bound on memory requirements and complexity for average-energy games with lower bounds, and analyzes multi-dimensional extensions.

Findings

Decidability of average-energy games with lower bounds is established.

Doubly-exponential upper bound on memory needed for winning strategies.

The problem is shown to be EXPSPACE-hard.

Abstract

We consider average-energy games, where the goal is to minimize the long-run average of the accumulated energy. While several results have been obtained on these games recently, decidability of average-energy games with a lower-bound constraint on the energy level (but no upper bound) remained open; in particular, so far there was no known upper bound on the memory that is required for winning strategies. By reducing average-energy games with lower-bounded energy to infinite-state mean-payoff games and analyzing the density of low-energy configurations, we show an almost tight doubly-exponential upper bound on the necessary memory, and that the winner of average-energy games with lower-bounded energy can be determined in doubly-exponential time. We also prove EXPSPACE-hardness of this problem. Finally, we consider multi-dimensional extensions of all types of average-energy games: without bounds, with only a lower bound, and with both a lower and an upper bound on the energy. We show that the fully-bounded version is the only case to remain decidable in multiple dimensions.