{"type":"ScholarlyArticle","title":"RLCard: A Platform for Reinforcement Learning in Card Games","authors":[{"name":"Daochen Zha","slug":"daochen-zha","given":"Daochen","family":"Zha","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Kwei-Herng Lai","slug":"kwei-herng-lai","given":"Kwei-Herng","family":"Lai","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Songyi Huang","slug":"songyi-huang","given":"Songyi","family":"Huang","affiliation":"Simon Fraser University , BC , Canada"},{"name":"Yuanpu Cao","slug":"yuanpu-cao","given":"Yuanpu","family":"Cao","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Keerthana Reddy","slug":"keerthana-reddy","given":"Keerthana","family":"Reddy","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Juan Vargas","slug":"juan-vargas","given":"Juan","family":"Vargas","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Alex Nguyen","slug":"alex-nguyen","given":"Alex","family":"Nguyen","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Ruzhe Wei","slug":"ruzhe-wei","given":"Ruzhe","family":"Wei","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Junyu Guo","slug":"junyu-guo","given":"Junyu","family":"Guo","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"},{"name":"Xia Hu","slug":"xia-hu","given":"Xia","family":"Hu","affiliation":"Department of Computer Science and Engineering , Texas A&M University , College Station , USA"}],"abstract":"We present RLCard, a Python platform for reinforcement learning research and development in card games. RLCard supports various card environments and several baseline algorithms with unified easy-to-use interfaces, aiming at bridging reinforcement learning and imperfect information games. The platform provides flexible configurations of state representation, action encoding, and reward design. RLCard also supports visualizations for algorithm debugging. In this demo, we showcase two representative environments and their visualization results. We conclude this demo with challenges and research opportunities brought by RLCard. A video is available on YouTube.","doi":"10.24963/ijcai.2020/764","url":"https://ijcai.org/proceedings/2020/764","pdf":"https://ijcai.org/proceedings/2020/0764.pdf","markdown":"https://ijcai.org/proceedings/2020/764.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/764","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":764,"pages":{"first":5264,"last":5266},"keywords":["Game Playing: general","Machine Learning: general","Multi-agent Systems: general"],"videos":[],"references":[{"position":1,"raw":"[Haarnoja et al., 2018] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In ICML, 2018.","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","authors":"Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Heinrich and Silver, 2016] Johannes Heinrich and David Silver. Deep reinforcement learning from self- play in imperfect-information games. arXiv preprint arXiv:1603.01121, 2016.","title":"Deep reinforcement learning from selfplay in imperfect-information games","authors":"Silver Heinrich, Johannes Heinrich, David Silver","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Lanctot et al., 2009] Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling. Monte carlo sampling for regret minimization in extensive games. In NeuIPS, 2009.","title":"Monte carlo sampling for regret minimization in extensive games","authors":"Marc Lanctot, Kevin Waugh, Martin Zinkevich, Michael Bowling","year":"2009","venue":"NeuIPS","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Lillicrap et al., 2015] Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous con- trol with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.","title":"Continuous control with deep reinforcement learning","authors":"Jonathan J Timothy P Lillicrap, Alexander Hunt, Nicolas Pritzel, Tom Heess, Yuval Erez, David Tassa, Daan Silver, Wierstra","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Mnih et al., 2015] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Belle- mare, Alex Graves, Martin Riedmiller, Andreas K Fidje- land, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015.","title":"Human-level control through deep reinforcement learning","authors":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Schulman et al., 2017] John Schulman, Filip Wolski, Pra- fulla Dhariwal, Alec Radford, and Oleg Klimov. Prox- imal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.","title":"Proximal policy optimization algorithms","authors":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Silver et al., 2017] David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. Nature, 550(7676):354, 2017.","title":"Mastering the game of go without human knowledge","authors":"David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton","year":"2017","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Sutton and Barto, 2018] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.","title":"Reinforcement learning: An introduction","authors":"S Richard, Andrew G Sutton, Barto","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Zha et al., 2019a] Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, and Xia Hu. Rlcard: A toolkit for reinforcement learning in card games. arXiv preprint arXiv:1910.04376, 2019.","title":"Rlcard: A toolkit for reinforcement learning in card games","authors":"Zha","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Zha et al., 2019b] Daochen Zha, Kwei-Herng Lai, Kaix- iong Zhou, and X. X. Hu. Experience replay optimization. In IJCAI, 2019.","title":"Experience replay optimization","authors":"Zha","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/589"},{"position":11,"raw":"[Zinkevich et al., 2008] Martin Zinkevich, Michael Johan- son, Michael Bowling, and Carmelo Piccione. Regret minimization in games with incomplete information. In NeuIPS, 2008.","title":"Regret minimization in games with incomplete information","authors":"Martin Zinkevich, Michael Johanson, Michael Bowling, Carmelo Piccione","year":"2008","venue":"NeuIPS","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}