Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Mguni, David Henry"'
Autor:
Li, Hanyu, Huang, Wenhan, Duan, Zhijian, Mguni, David Henry, Shao, Kun, Wang, Jun, Deng, Xiaotie
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics a
Externí odkaz:
http://arxiv.org/abs/2312.11063
Autor:
Yan, Xue, Song, Yan, Cui, Xinyu, Christianos, Filippos, Zhang, Haifeng, Mguni, David Henry, Wang, Jun
Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having high-quality prompts on hand, however, is vital to the framework's ef
Externí odkaz:
http://arxiv.org/abs/2310.18127
Autor:
Slumbers, Oliver, Mguni, David Henry, McAleer, Stephen Marcus, Blumberg, Stefano B., Wang, Jun, Yang, Yaodong
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game theory (GT) assumes that agents are not affected by risk from other age
Externí odkaz:
http://arxiv.org/abs/2205.15434
Autor:
Mguni, David Henry, Jafferjee, Taher, Wang, Jianhong, Slumbers, Oliver, Perez-Nieves, Nicolas, Tong, Feifei, Yang, Li, Zhu, Jiangcheng, Yang, Yaodong, Wang, Jun
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to jointly achieve optimal outcomes. In this paper, we introduce a new general
Externí odkaz:
http://arxiv.org/abs/2112.02618
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algorithm. In this setting, we first demonstrate that MDP-Expert, an existi
Externí odkaz:
http://arxiv.org/abs/2110.03604
Autor:
Kuba, Jakub Grudzien, Wen, Muning, Yang, Yaodong, Meng, Linghui, Gu, Shangding, Zhang, Haifeng, Mguni, David Henry, Wang, Jun
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectivenes
Externí odkaz:
http://arxiv.org/abs/2108.08612
Autor:
Nieves, Nicolas Perez, Yang, Yaodong, Slumbers, Oliver, Mguni, David Henry, Wen, Ying, Wang, Jun
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissors). Yet, there is a lack of rigorous treatment for defining diversity
Externí odkaz:
http://arxiv.org/abs/2103.07927
Autor:
Dinh, Le Cong, Yang, Yaodong, McAleer, Stephen, Tian, Zheng, Nieves, Nicolas Perez, Slumbers, Oliver, Mguni, David Henry, Ammar, Haitham Bou, Wang, Jun
Publikováno v:
Transactions on Machine Learning Research 2022
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learning algorithms for solving two-player zero-sum normal-form games where t
Externí odkaz:
http://arxiv.org/abs/2103.07780
Autor:
Li, Hanyu, Huang, Wenhan, Duan, Zhijian, Mguni, David Henry, Shao, Kun, Wang, Jun, Deng, Xiaotie
Publikováno v:
In Computer Science Review February 2024 51
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