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pro vyhledávání: '"Diao, Xingrong"'
Autor:
Diao, Xingrong, Wang, Jiankun
In this article, we present an end-to-end collision avoidance policy based on deep reinforcement learning (DRL) for multi-agent systems, demonstrating encouraging outcomes in real-world applications. In particular, our policy calculates the control c
Externí odkaz:
http://arxiv.org/abs/2408.16370
Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of the path planning, collision detection is the most time-consuming operation. In this paper, we propose a learni
Externí odkaz:
http://arxiv.org/abs/2309.14845
Publikováno v:
In Biomimetic Intelligence and Robotics March 2024 4(1)