  We introduce a human-compatible reinforcement-learning approach to a
cooperative game, making use of a third-party hand-coded human-compatible bot
to generate initial training data and to perform initial evaluation. Our
learning approach consists of imitation learning, search, and policy iteration.
Our trained agents achieve a new state-of-the-art for bridge bidding in three
settings: an agent playing in partnership with a copy of itself; an agent
partnering a pre-existing bot; and an agent partnering a human player.
