Nov 15, 2018 · In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to ...
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control.
May 2, 2024 · Abstract: Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control.
We show that our approach substantially reduces the human intervention and improves the performance in autonomous navigation1, at the same time it ensures ...
In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to ...
The Intervention Aided Reinforcement Learning (IARL) framework is proposed, which utilizes human intervened robot-environment interaction to improve the ...
Nov 15, 2018 · In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment ...
2021. Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation. F Wang, B Zhou, K Chen, T Fan, X Zhang, J Li, H Tian ...
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Oct 1, 2024 · Deep Reinforcement Learning (DRL) offers a powerful approach to training neural network control policies for stochastic queuing networks ...
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL).