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pro vyhledávání: '"Bui, The Viet"'
Offline reinforcement learning (RL) has garnered significant attention for its ability to learn effective policies from pre-collected datasets without the need for further environmental interactions. While promising results have been demonstrated in
Externí odkaz:
http://arxiv.org/abs/2410.01954
This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems. The learning problem under consideration poses several challenges, characterized by high-dime
Externí odkaz:
http://arxiv.org/abs/2310.06801
Training agents in multi-agent competitive games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by opponents' strategies. Existing methods of
Externí odkaz:
http://arxiv.org/abs/2308.10188
Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim agent) to perform poorly in a multi-agent environment. In existing studie
Externí odkaz:
http://arxiv.org/abs/2210.16915
We study inverse reinforcement learning (IRL) and imitation learning (IM), the problems of recovering a reward or policy function from expert's demonstrated trajectories. We propose a new way to improve the learning process by adding a weight functio
Externí odkaz:
http://arxiv.org/abs/2208.09611
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue would be intractab
Externí odkaz:
http://arxiv.org/abs/2204.12992
Autor:
Foster, Kim, Shochet, Ian, Shakespeare-Finch, Jane, Maybery, Darryl, Bui, Minh Viet, Gordon, Ian, Bagot, Kathleen L., Roche, Michael
Publikováno v:
In International Journal of Nursing Studies November 2024 159
Publikováno v:
Eur. Phys. J. Plus (2021) 136:109
A non-relativistic scalar particle moving on a curved surface undergoes a geometric scattering whose behavior is sensitive to the theoretically ambiguous values of the intrinsic and extrinsic curvature coefficients entering the expression for the qua
Externí odkaz:
http://arxiv.org/abs/2012.06395
Autor:
Bui, The Viet, Le-Hong, Phuong
The FPT.AI team participated in the SHINRA2020-ML subtask of the NTCIR-15 SHINRA task. This paper describes our method to solving the problem and discusses the official results. Our method focuses on learning cross-lingual representations, both on th
Externí odkaz:
http://arxiv.org/abs/2010.03424
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