Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Xiang, Violet"'
Multi-agent reinforcement learning (MARL) methods struggle with the non-stationarity of multi-agent systems and fail to adaptively learn online when tested with novel agents. Here, we leverage large language models (LLMs) to create an autonomous agen
Externí odkaz:
http://arxiv.org/abs/2407.07086
Autor:
Long, Bria, Xiang, Violet, Stojanov, Stefan, Sparks, Robert Z., Yin, Zi, Keene, Grace E., Tan, Alvin W. M., Feng, Steven Y., Zhuang, Chengxu, Marchman, Virginia A., Yamins, Daniel L. K., Frank, Michael C.
Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''data gap'' is a key challenge both for building intelligent artificial
Externí odkaz:
http://arxiv.org/abs/2406.10447
In real-world environments, autonomous agents rely on their egocentric observations. They must learn adaptive strategies to interact with others who possess mixed motivations, discernible only through visible cues. Several Multi-Agent Reinforcement L
Externí odkaz:
http://arxiv.org/abs/2312.08662
Autor:
Long, Bria, Goodin, Sarah, Kachergis, George, Marchman, Virginia A., Radwan, Samaher F., Sparks, Robert Z., Xiang, Violet, Zhuang, Chengxu, Hsu, Oliver, Newman, Brett, Yamins, Daniel L. K., Frank, Michael C.
Publikováno v:
Behavior Research Methods; Apr2024, Vol. 56 Issue 4, p3523-3534, 12p
Publikováno v:
In Cognition July 2020 200
Akademický článek
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Autor:
Zhuang C; Department of Psychology, Stanford University.; Department of Brain and Cognitive Sciences, MIT., Xiang V; Department of Psychology, Stanford University., Bai Y; Department of Brain and Cognitive Sciences, MIT., Jia X; School of Life Sciences, Tsinghua University., Turk-Browne N; Department of Psychology and Wu Tsai Institute, Yale University., Norman K; Department of Psychology and Princeton Neuroscience Institute, Princeton University., DiCarlo JJ; Department of Brain and Cognitive Sciences, MIT., Yamins DLK; Department of Psychology, Stanford University.; Computer Science, Stanford University.; Wu Tsai Neurosciences Institute, Stanford University.
Publikováno v:
Advances in neural information processing systems [Adv Neural Inf Process Syst] 2022; Vol. 35, pp. 22628-22642.