Exploring a Learning Architecture for General Game Playing

General Game Playing (GGP) is a platform for developing general Artificial Intelligence algorithms to play a large variety of games that are unknown to players in advance. This paper describes and analyses GGPZero, a learning architecture for GGP, inspired by the success of AlphaGo and AlphaZero. GGPZero takes as input a previously unknown game description and constructs a deep neural network to be trained using self-play together with Monte-Carlo Tree Search. The general architecture of GGPZero is similar to that of Goldwaser and Thielscher (2020) [4] with the main differences in the choice of the GGP reasoner and the neural network construction; furthermore, we explore additional experimental evaluation strategies. Our main contributions are: confirming the feasibility of deep reinforcement for GGP, analysing the impact of the type and depth of the underlying neural network, and investigating simulation vs. time limitations on training.