Reinforcement Learning for Sports Analytics

体育分析的强化学习

• 批准号: 521357-2018
• 负责人: Poupart, Pascal
• 金额: $ 14.53万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Strategic Projects - Group
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=694176
• 关键词: Reinforcement; Learning; Sports; Analytics

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios. We will advance the state of the art in deep reinforcement learning methods to enable analysis of continuous-flow input data. The collaboration with our industrial partner will tackle open problems in deep reinforcement learning that can result in richer capabilities in sports analytics for ice hockey and other continuous-flow sports such as basketball and soccer. Deep reinforcement learning, is a breakthrough technology with prominent successes in games such as Go (AlphaGo) and Chess (AlphaZero). This project will push deep reinforcement learning further into the physical world, with multi-person, complex scenarios arising from real-world noisy data sources. We will develop fundamental algorithmic advances and apply them to tasks including: - player evaluation - event predictions (match outcomes, next action, expected scores) - recognizing types of players, teams, play sequences, and tactics - identifying characteristic strengths and weaknesses of players and teams. Our partner is the Montreal-based company SPORTLOGiQ, which uses advanced computer vision to extract information about events from video of sports matches. Their information is more detailed than that provided by any other company or organization. The market for sports analytics is growing rapidly, with major international companies receiving millions of investment dollars. This project will build significant Canadian capacity in sports analytics, support academic research, advance commercialization in the sports industry, and train highly qualified personnel. Canada has achieved a position of leadership in reinforcement learning, through excellent researchers who have attracted companies such as Google's DeepMind to set up Canadian labs. The proposed research will contribute to Canada's reinforcement learning ecosystem by establishing novel algorithmic contributions for a major application area with great commercial potential.

我们的项目开发了新的机器学习算法，用于解释复杂的多智能体场景。 我们将推进深度强化学习方法的最新技术，以实现对连续流输入数据的分析。 与我们的工业合作伙伴的合作将解决深度强化学习中的开放性问题，这些问题可以为冰球和其他连续流运动（如篮球和足球）提供更丰富的体育分析能力。 深度强化学习是一项突破性的技术，在围棋（AlphaGo）和国际象棋（AlphaZero）等游戏中取得了显著的成功。该项目将把深度强化学习进一步推向物理世界，包括来自真实世界噪声数据源的多人复杂场景。 我们将开发基本的算法进步，并将其应用到任务中，包括：-球员评估-事件预测（比赛结果，下一步行动，预期得分）-识别球员，球队，比赛序列和战术的类型-识别球员和球队的特征优势和弱点。我们的合作伙伴是总部位于伦敦的SPORTLOGiQ公司，该公司使用先进的计算机视觉从体育比赛视频中提取有关事件的信息。他们的信息比任何其他公司或组织提供的信息都要详细。体育分析市场正在迅速增长，主要的国际公司获得了数百万美元的投资。该项目将建立加拿大在体育分析方面的重要能力，支持学术研究，促进体育产业的商业化，并培养高素质的人才。加拿大在强化学习方面取得了领先地位，通过优秀的研究人员吸引了谷歌的DeepMind等公司建立加拿大实验室。拟议的研究将通过为具有巨大商业潜力的主要应用领域建立新的算法贡献，为加拿大的强化学习生态系统做出贡献。