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Philip Paquette

Alumni

Publications

No Press Diplomacy: Modeling Multi-Agent Gameplay
Philip Paquette
Yuchen Lu
Steven Bocco
Max O. Smith
Satya Ortiz-Gagné
Jonathan K. Kummerfeld
Satinder Singh
Joelle Pineau
Aaron Courville
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Rel… (voir plus)iance on trust and coordination makes Diplomacy the first non-cooperative multi-agent benchmark for complex sequential social dilemmas in a rich environment. In this work, we focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players. We present DipNet, a neural-network-based policy model for No Press Diplomacy. The model was trained on a new dataset of more than 150,000 human games. Our model is trained by supervised learning (SL) from expert trajectories, which is then used to initialize a reinforcement learning (RL) agent trained through self-play. Both the SL and RL agents demonstrate state-of-the-art No Press performance by beating popular rule-based bots.
2018-12-31
Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (publié)
doi.org
arxiv.org