Minimal öznitelik mühendisliği ve derin pekiştirmeli öğrenme kullanarak briç deklarasyon oyuncusu eğitimi

The game of contract bridge, or just bridge, is a four-player imperfect information card game where two partnerships of two players compete against each other. It has two main phases: bidding and play. While the computer players have approached human-level performance two decades ago in the playing phase, bidding is still a very challenging problem. This makes bridge one of the last popular games where computers still lag behind the expert human-level performance. During bidding, players only know their own cards while participating in a public auction. Performing well in this phase requires the players to figure out how to communicate with their partners using the limited vocabulary of bids to decide on a joint contract. This communication is restricted by the strict ordering of legal bids and can be negatively interfered by bids made by the opponent partnership. In this thesis, we experiment with several novel architectures with minimal feature engineering and evaluate them by using supervised training over a data set of expert-level human games. After that, we further study different forms of deep reinforcement learning to refine the resulting model by simulated gameplay. Lastly, we propose an oracle evaluation metric that can measure the quality of any bidding sequence with respect to the game-theoretical optimum.