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arXiv · 2011.14124

Human-Agent Cooperation in Bridge Bidding

Edward Lockhart·Neil Burch·Nolan Bard·Sebastian Borgeaud·Tom Eccles·Lucas Smaira·Ray Smith
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Abstract

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.

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Edward Lockhart, Neil Burch, Nolan Bard, Sebastian Borgeaud, Tom Eccles, Lucas Smaira, Ray Smith. 2020-11-28. Human-Agent Cooperation in Bridge Bidding. https://arxiv.org/abs/2011.14124

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