Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Qiao, Zhongjian"'
Autor:
Liu, Qi, Gao, Jianqi, Zhu, Dongjie, Qiao, Zhongjian, Chen, Pengbin, Guo, Jingxiang, Li, Yanjie
Multi-agent target assignment and path planning (TAPF) are two key problems in intelligent warehouse. However, most literature only addresses one of these two problems separately. In this study, we propose a method to simultaneously solve target assi
Externí odkaz:
http://arxiv.org/abs/2408.13750
The performance of offline reinforcement learning (RL) suffers from the limited size and quality of static datasets. Model-based offline RL addresses this issue by generating synthetic samples through a dynamics model to enhance overall performance.
Externí odkaz:
http://arxiv.org/abs/2408.12970
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of MFRL algorithms. Previous studies have
Externí odkaz:
http://arxiv.org/abs/2310.15017
Publikováno v:
In Progress in Nuclear Energy December 2024 177
