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Volume 11 Issue 10
Oct.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(10): 2111-2122
Z. Qiu, S. Wang, D. You, and  M. C. Zhou,  “Bridge bidding via deep reinforcement learning and belief Monte Carlo search,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 10, pp. 2111–2122, Oct. 2024. doi: 10.1109/JAS.2024.124488
Citation: Z. Qiu, S. Wang, D. You, and  M. C. Zhou,  “Bridge bidding via deep reinforcement learning and belief Monte Carlo search,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 10, pp. 2111–2122, Oct. 2024. doi: 10.1109/JAS.2024.124488
shu

Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search

doi: 10.1109/JAS.2024.124488
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    Abstract

  • Contract Bridge, a four-player imperfect information game, comprises two phases: bidding and playing. While computer programs excel at playing, bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents. In this work, we introduce a Bridge bidding agent that combines supervised learning, deep reinforcement learning via self-play, and a test-time search approach. Our experiments demonstrate that our agent outperforms WBridge5, a highly regarded computer Bridge software that has won multiple world championships, by a performance of 0.98 IMPs (international match points) per deal over

    10 000

    deals, with a much cost-effective approach. The performance significantly surpasses previous state-of-the-art (0.85 IMPs per deal). Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

     

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    Highlights

    • A cost-effective deep reinforcement learning approach for training a Bridge bidding agent is proposed. Unlike the training procedures in previous work, which utilized thousands of virtual machines for acting, our method achieves better results using a single machine with multiple acting threads
    • A novel search-based method which integrates a belief network to predict cards of other players and a policy network to evaluate candidate actions is proposed. This method improves the performance of agent at test-time further than previous work
    • A tournament between trained Bridge bidding agents and WBridge5, an award-winning Bridge software is conducted. The analysis on the deals played in tournament is performed and shows the high-level strength of the trained agents

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