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主题: Scalable and Robust Multi-Agent Reinforcement Learning

简介: 本演讲将涵盖我们最近的多智能体强化学习方法,这些方法用于协调沟通有限或没有交流的智能体团队。这些方法将包括深入的多主体增强学习方法和学习异步策略的分层方法,这些方法实际上允许针对不同主体在不同时间进行学习和/或执行。这些方法可扩展到较大的空间和视野,并且对于其他代理学习引起的非平稳性具有鲁棒性。将显示来自基准域和多机器人域的结果。

作者简介: Christopher Amato,美国东北大学教授,研究兴趣包括人工智能,机器人技术,多智能体和多机器人系统,不确定性下的推理,博弈论和机器学习。

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最新论文

This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels. These affinity nodes are selected based on a better affiliation of superpixels, namely subspace-preserving representation which is generated by sparse subspace clustering based on subspace pursuit. Then a KSC-graph is built via a novel kernel spectral clustering to explore the nonlinear relationships among these affinity nodes. Moreover, an adjacency-graph at each scale is constructed, which is further used to update the proposed KSC-graph at affinity nodes. The fusion graph is built across different scales, and it is partitioned to obtain final segmentation result. Experimental results on the Berkeley segmentation dataset and Microsoft Research Cambridge dataset show the superiority of our framework in comparison with the state-of-the-art methods. The code is available at https://github.com/Yangzhangcst/AF-graph.

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