Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Shi, Rongye"'
Autor:
Feng, Pu, Liang, Junkang, Wang, Size, Yu, Xin, Ji, Xin, Chen, Yiting, Zhang, Kui, Shi, Rongye, Wu, Wenjun
In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in training versus reliance on local observations in execution, lacking
Externí odkaz:
http://arxiv.org/abs/2407.08164
Incorporating symmetry as an inductive bias into multi-agent reinforcement learning (MARL) has led to improvements in generalization, data efficiency, and physical consistency. While prior research has succeeded in using perfect symmetry prior, the r
Externí odkaz:
http://arxiv.org/abs/2401.00167
Multi-agent reinforcement learning (MARL) has achieved promising results in recent years. However, most existing reinforcement learning methods require a large amount of data for model training. In addition, data-efficient reinforcement learning requ
Externí odkaz:
http://arxiv.org/abs/2307.16186
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks
Externí odkaz:
http://arxiv.org/abs/2303.02063
Autor:
Apps, Richard, Biancotto, Angélique, Candia, Julián, Kotliarov, Yuri, Perl, Shira, Cheung, Foo, Farmer, Rohit, Mulè, Matthew P., Rachmaninoff, Nicholas, Chen, Jinguo, Martins, Andrew J., Shi, Rongye, Zhou, Huizhi, Bansal, Neha, Schum, Paula, Olnes, Matthew J., Milanez-Almeida, Pedro, Han, Kyu Lee, Sellers, Brian, Cortese, Mario, Hagan, Thomas, Rouphael, Nadine, Pulendran, Bali, King, Lisa, Manischewitz, Jody, Khurana, Surender, Golding, Hana, van der Most, Robbert G., Dickler, Howard B., Germain, Ronald N., Schwartzberg, Pamela L., Tsang, John S.
Publikováno v:
In Cell Reports 24 September 2024 43(9)
Autor:
Song, Hannah W., Prochazkova, Michaela, Shao, Lipei, Traynor, Roshini, Underwood, Sarah, Black, Mary, Fellowes, Vicki, Shi, Rongye, Pouzolles, Marie, Chou, Hsien-Chao, Cheuk, Adam T., Taylor, Naomi, Jin, Ping, Somerville, Robert P., Stroncek, David F., Khan, Javed, Highfill, Steven L.
Publikováno v:
In Cytotherapy July 2024 26(7):757-768
Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small data. To mitigate these limitations, recent studies introduced a hybri
Externí odkaz:
http://arxiv.org/abs/2106.03142
Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., density) on road segments using partially observed data, plays an important role on efficient traffic control and operation that intelligent transportation systems (ITS)
Externí odkaz:
http://arxiv.org/abs/2101.06580
Car-following behavior has been extensively studied using physics-based models, such as the Intelligent Driver Model. These models successfully interpret traffic phenomena observed in the real-world but may not fully capture the complex cognitive pro
Externí odkaz:
http://arxiv.org/abs/2012.13376
Autor:
Mamo, Theodros, Cox, Cheryl A., Demorest, Connor, Fontaine, Magali J., Hubel, Allison, Kelley, Linda, Khan, Aisha, Marks, Denese C., Pati, Shibani, Reems, Jo-Anna, Spohn, Gabriele, Schäfer, Richard, Shi, Rongye, Shao, Lipei, Stroncek, David, McKenna, David H.
Publikováno v:
In Cytotherapy July 2024