Strategy Complexity of Reachability in Countable Stochastic 2-Player Games

We study countably infinite stochastic 2-player games with reachability objectives. Our results provide a complete picture of the memory requirements of $\varepsilon$-optimal (resp. optimal) strategies. These results depend on whether the game graph is infinitely branching and on whether one requires strategies that are uniform (i.e., independent of the start state). Our main result is that $\varepsilon$-optimal (resp. optimal) Maximizer strategies in infinitely branching turn-based reachability games require infinite memory. This holds even under very strong restrictions. Even if all states have an almost surely winning Maximizer strategy, strategies with a step counter plus finite private memory are still useless. Regarding uniformity, we show that for Maximizer there need not exist positional uniformly $\varepsilon$-optimal strategies even in finitely branching turn-based games, whereas there always exists one that uses one bit of public memory, even in concurrent games with finite action sets.