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Towards effective learning in Monte Carlo Tree Search

Towards effective learning in Monte Carlo Tree Search
蒙特卡罗树搜索中的有效学习
批准号:
556170-2020
负责人:
Müller, Martin
金额:
$2.91万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2021
资助国家:
加拿大
项目状态:
已结题
起止时间:
2021-01-01 至 2022-12-31

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中文摘要
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英文摘要
The combination of machine learning and heuristic search methods has achieved groundbreaking successes in recent years. Some of the best-known examples are the AlphaGo and Alpha Zero architectures recently developed by DeepMind. These approaches to computer problem-solving combine the search method of Monte Carlo Tree Search (MCTS) with the machine learning method of Deep Reinforcement Learning.We propose research which generalizes the Alpha Zero search and learning approach, and addresses some of its shortcomings: 1. generalizing learning in MCTS to domains without a perfect simulation model, and 2. performing an in-depth study of the positive interaction between learning and search in the context of MCTS, and of possible bottlenecks for learning.
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Exploration and Learning in Heuristic Search
  • 批准号:
    RGPIN-2020-04048
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.55万
  • 财政年份:
    2022
  • 负责人:
    Müller, Martin
  • 依托单位:
Exploration and Learning in Heuristic Search
  • 批准号:
    RGPIN-2020-04048
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.55万
  • 财政年份:
    2021
  • 负责人:
    Müller, Martin
  • 依托单位:
Towards effective learning in Monte Carlo Tree Search
  • 批准号:
    556170-2020
  • 项目类别:
    Alliance Grants
  • 资助金额:
    $2.91万
  • 财政年份:
    2020
  • 负责人:
    Müller, Martin
  • 依托单位:
Exploration and Learning in Heuristic Search
  • 批准号:
    RGPIN-2020-04048
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.55万
  • 财政年份:
    2020
  • 负责人:
    Müller, Martin
  • 依托单位:
国内基金
海外基金
多跳无线 MESH 网络中 QoS 保障算法的研究设计和性能分析