  This paper introduces a simple and fast variant of Planet Wars as a test-bed
for statistical planning based Game AI agents, and for noisy hyper-parameter
optimisation. Planet Wars is a real-time strategy game with simple rules but
complex game-play. The variant introduced in this paper is designed for speed
to enable efficient experimentation, and also for a fixed action space to
enable practical inter-operability with General Video Game AI agents. If we
treat the game as a win-loss game (which is standard), then this leads to
challenging noisy optimisation problems both in tuning agents to play the game,
and in tuning game parameters. Here we focus on the problem of tuning an agent,
and report results using the recently developed N-Tuple Bandit Evolutionary
Algorithm and a number of other optimisers, including Sequential Model-based
Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit
Evolutionary offers competitive performance as well as insight into the effects
of combinations of parameter choices.
