Hidden Information General Game Playing with Deep Learning and Search

General Game Playing agents are capable of learning to play games they have never seen before, merely by looking at a formal description of the rules of a game. Recent developments in deep learning have influenced the way state-of-the-art AI systems can learn to play games with perfect information like Chess and Go. This development is popularised by the success of AlphaZero and was subsequently generalised to arbitrary games describable in the general Game Description Language, GDL. Many real-world problems, however, are non-deterministic and involve actors with concealed information, or events with probabilistic outcomes. We describe a framework and system for General Game Playing with self-play reinforcement learning and search for hidden-information games, which can be applied to any game describable in the extended Game Description Language for imperfect-information games, GDL-II.