no code implementations • 22 Jan 2024 • Kristina Dzeparoska, Jieyu Lin, Ali Tizghadam, Alberto Leon-Garcia
And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand.
no code implementations • 9 Jan 2022 • Sai Qian Zhang, Jieyu Lin, Qi Zhang
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data.
no code implementations • ACL 2021 • Jieyu Lin, Jiajie Zou, Nai Ding
We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options.
1 code implementation • NeurIPS 2020 • Sai Qian Zhang, Jieyu Lin, Qi Zhang
Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
1 code implementation • 8 Mar 2020 • Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot
Our results on the StartCraft II multi-agent benchmark demonstrate that c-MARL teams are highly vulnerable to perturbations applied to one of their agent's observations.
Multi-agent Reinforcement Learning reinforcement-learning +1
2 code implementations • NeurIPS 2019 • Sai Qian Zhang, Qi Zhang, Jieyu Lin
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications.
Multi-agent Reinforcement Learning reinforcement-learning +3