  Diplomacy is a seven-player non-stochastic, non-cooperative game, where
agents acquire resources through a mix of teamwork and betrayal. Reliance 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.
