Rinascimento: Optimising Statistical Forward Planning Agents for Playing
  Splendor
  Game-based benchmarks have been playing an essential role in the development
of Artificial Intelligence (AI) techniques. Providing diverse challenges is
crucial to push research toward innovation and understanding in modern
techniques. Rinascimento provides a parameterised partially-observable
multiplayer card-based board game, these parameters can easily modify the
rules, objectives and items in the game. We describe the framework in all its
features and the game-playing challenge providing baseline game-playing AIs and
analysis of their skills. We reserve to agents' hyper-parameter tuning a
central role in the experiments highlighting how it can heavily influence the
performance. The base-line agents contain several additional contribution to
Statistical Forward Planning algorithms.
