6篇
您的检索式：作者名="Ying Linghui"
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| 1 | Photostabilisation of poly (p-phenylenebenzo-bisoxazole) fibre 显示文摘 | Song Bo Zhuang Qixin Ying Linghui | 2012 | Polymer Degradation and Stability2012，97，9： | 1 |
| 2 | Study on photoaging of poly(p-phenylene benzobisoxazole) fiber显示文摘 | Song Bo Fu Qian Ying Linghui | 2012 | Journal ot' Applied Polymer Science2012，124，2： | 1 |
| 3 | Photostabilisation of poly (p phenylenebenzo-bisoxazole) fibre 显示文摘 | Song Bo Zhuang Qixin Ying Linghui | 2012 | Poly- mer Degradation and Stability2012，97，9： | 1 |
| 4 | Study on photoaging of poly (p-phenylene benzobisoxazole) fiber 显示文摘 | Song Bo Fu Qian Ying Linghui | 2012 | Journal of Applied Polymer Science2012，124，2： | 1 |
| 5 | Study on Photoaging of Poly(p-phenylene benzobisoxazole) Fiber显示文摘 | Song Bo Fu Qian Ying Linghui | 2012 | Journal of Applied Polymer Science2012，124，： | 1 |
| 6 | Offline Pre-trained Multi-agent Decision Transformer显示文摘Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment.Such a paradigm is also desirable for multi-agent reinforcement learning(MARL)tasks,given the combinatorially increased interactions among agents and with the environment.However,in MARL,the paradigm of offline pre-training with online fine-tuning has not been studied,nor even datasets or benchmarks for offline MARL research are available.In this paper,we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of MARL.We investigate the generalization of MARL offline pre-training in the following three aspects:1)between single agents and multiple agents,2)from offline pretraining to online fine tuning,and 3)to that of multiple downstream tasks with few-shot and zero-shot capabilities.We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment,and then propose the novel architecture of multi-agent decision transformer(MADT)for effective offline learning.MADT leverages the transformer′s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks.A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios.On the StarCraft II offline dataset,MADT outperforms the state-of-the-art offline reinforcement learning(RL)baselines,including BCQ and CQL.When applied to online tasks,the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot cases.To the best of our knowledge,this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL. | Linghui Meng Muning Wen Chenyang Le Xiyun Li Dengpeng Xing Weinan Zhang Ying Wen Haifeng Zhang Jun Wang Yaodong Yang Bo Xu | 2023 | Machine Intelligence Research2023，20，2： | 0 |
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