Rinascimento: using event-value functions for playing Splendor
  In the realm of games research, Artificial General Intelligence algorithms
often use score as main reward signal for learning or playing actions. However
this has shown its severe limitations when the point rewards are very rare or
absent until the end of the game. This paper proposes a new approach based on
event logging: the game state triggers an event every time one of its features
changes. These events are processed by an Event-value Function (EF) that
assigns a value to a single action or a sequence. The experiments have shown
that such approach can mitigate the problem of scarce point rewards and improve
the AI performance. Furthermore this represents a step forward in controlling
the strategy adopted by the artificial agent, by describing a much richer and
controllable behavioural space through the EF. Tuned EF are able to neatly
synthesise the relevance of the events in the game. Agents using an EF show
more robust when playing games with several opponents.
