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Nov, 2022
从完整状态轨迹中发现无监督行为
Discovering Unsupervised Behaviours from Full-State Trajectories
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Luca Grillotti, Antoine Cully
TL;DR
本文介绍了一种利用自主生成的行为特征描述模拟机器人环境中任务的质量-多样性(Quality-Diversity)算法,可以自主发现各种解决方案来处理导航、高速前进和半滚动任务。
Abstract
Improving
open-ended learning
capabilities is a promising approach to enable robots to face the unbounded complexity of the real-world. Among existing methods, the ability of
quality-diversity algorithms
to gener
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