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Reinforcement Learning in Complex Environments through Multiple Adaptive Partitions

机译：通过多个自适应分区在复杂环境中进行强化学习

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Andrea Bonarini; Ro Lazaric; Marcello Restelli;
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年度 2008
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1. A proposition of adaptive state space partition in reinforcement learning with Voronoi tessellation [J] . Takayasu Fuchida, Kathy Thi Aung Artificial life and robotics . 2013,第3a4期

机译：Voronoi镶嵌强化学习中的自适应状态空间划分的命题
2. Adaptive state space partitioning for reinforcement learning [J] . Ivan S. K. Lee, Henry Y. K. Lau Engineering Applications of Artificial Intelligence . 2004,第6期

机译：自适应状态空间划分，用于强化学习
3. Learning adversarial policy in multiple scenes environment via multi-agent reinforcement learning [J] . Li Yang, Wang Xinzhi, Wang Wei, Connection Science . 2021,第3期

机译：通过多功能钢筋学习在多个场景环境中学习对抗性政策
4. Reinforcement Learning in Complex Environments Through Multiple Adaptive Partitions [C] . Andrea Bonarini, Alessandro Lazaric, Marcello Restelli Italian Association for Artificial Intelligence Congress; 20070910-13; Rome(IT) . 2007

机译：通过多个自适应分区在复杂环境中进行强化学习
5. Adaptive sensing in uncertain environments: Maximum likelihood, sensor networks, and reinforcement learning. [D] . Blatt, Doron. 2006

机译：不确定环境中的自适应感测：最大可能性，传感器网络和强化学习。
6. Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments [O] . Zijian Hu, Kaifang Wan, Xiaoguang Gao, 2020

机译：具有多种经验库的深度强化学习方法用于复杂未知环境中的无人机自主运动计划
7. A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method [O] . Yi Zhu, Fang-Bao Tian, John Young, 2021

机译：深增强学习复杂环境中鱼适应行为的数值研究与浸入边界 - 格子Boltzmann方法

Reinforcement Learning in Complex Environments through Multiple Adaptive Partitions

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