1 code implementation • 15 Dec 2021 • Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, Lei Zhang
Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions.
no code implementations • NeurIPS 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 29 Sep 2021 • Runsheng Yu, Xinrun Wang, James Kwok
Most advanced Actor-Critic (AC) approaches update the actor and critic concurrently through (stochastic) Gradient Descents (GD), which may be trapped into bad local optimality due to the instability of these simultaneous updating schemes.
no code implementations • CVPR 2022 • Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, XiaoLi Li
In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.
no code implementations • 16 Feb 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 8 Jan 2021 • Runsheng Yu, Yu Gong, Rundong Wang, Bo An, Qingwen Liu, Wenwu Ou
Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.
no code implementations • 1 Jan 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 22 Dec 2020 • Runsheng Yu, Yu Gong, Xu He, Bo An, Yu Zhu, Qingwen Liu, Wenwu Ou
Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.
no code implementations • 21 Aug 2020 • Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang
Thus, the global policy of the whole page could be sub-optimal.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 18 Nov 2019 • Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen Hou, Hanjiang Lai, Bo An
Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.
no code implementations • ICML 2020 • Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich
Under the limited bandwidth constraint, a communication protocol is required to generate informative messages.
no code implementations • NeurIPS 2018 • Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, Bo Zhang
Based on these sub-images, a local exposure for each sub-image is automatically learned by virtue of policy network sequentially while the reward of learning is globally designed for striking a balance of overall exposures.