No Press Diplomacy
This thesis presents an article on an agent which can play the "No-Press" version (without messages) of the Diplomacy board game. Diplomacy is a 7-player negotiation game where each player tries to conquer the majority of the supply centers in Europe at the beginning of the 20th century.  The article first presents a novel dataset of more than 150 000 human games. This dataset was compiled following the signing of a partnership with an external site. The games, which were played on this platform, were all converted into a new standardized format and then replayed to ensure their quality. The article also presents a game engine, with a web interface, allowing humans to play against the models that have been trained.  Moreover, the article presents a supervised learning model where an agent learns to reproduce the behavior of all players in the dataset by maximum likelihood. An agent that learns by reinforcement (by playing games against itself) has also been trained. The article concludes by doing an analysis of these models and comparing their performance against complex rule-based agents.
Diplomacy negotiation game supervised learning reinforcement learning deep learning