  We propose "semantic labelling" as a novel ingredient for solving games in
the context of LTL synthesis. It exploits recent advances in the automata-based
approach, yielding more information for each state of the generated parity game
than the game graph can capture. We utilize this extra information to improve
standard approaches as follows. (i) Compared to strategy improvement (SI) with
random initial strategy, a more informed initialization often yields a winning
strategy directly without any computation. (ii) This initialization makes SI
also yield smaller solutions. (iii) While Q-learning on the game graph turns
out not too efficient, Q-learning with the semantic information becomes
competitive to SI. Since already the simplest heuristics achieve significant
improvements the experimental results demonstrate the utility of semantic
labelling. This extra information opens the door to more advanced learning
approaches both for initialization and improvement of strategies.
