Rinascimento: using event-value functions for playing Splendor
This addresses a specific bottleneck in game AI for researchers, offering an incremental improvement over traditional score-based methods.
The paper tackles the problem of rare or delayed point rewards in game AI by proposing event-value functions that assign values to game events, which improved AI performance and provided richer behavioral control.
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.