no code implementations • 11 Jan 2024 • Yuanzhao Zhai, Yiying Li, Zijian Gao, Xudong Gong, Kele Xu, Dawei Feng, Ding Bo, Huaimin Wang
ORPO generates Optimistic model Rollouts for Pessimistic offline policy Optimization.
no code implementations • 17 Sep 2023 • Junjie Zhu, Yiying Li, Chunping Qiu, Ke Yang, Naiyang Guan, Xiaodong Yi
In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen.
no code implementations • 24 Aug 2022 • Zijian Gao, Yiying Li, Kele Xu, Yuanzhao Zhai, Dawei Feng, Bo Ding, XinJun Mao, Huaimin Wang
The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.
no code implementations • 21 May 2022 • Chao Chen, Zijian Gao, Kele Xu, Sen yang, Yiying Li, Bo Ding, Dawei Feng, Huaimin Wang
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
no code implementations • 7 Jul 2021 • Wei Zhou, Yiying Li
Gradient Episodic Memory is indeed a novel method for continual learning, which solves new problems quickly without forgetting previously acquired knowledge.
no code implementations • 25 May 2021 • Zijian Gao, Kele Xu, Bo Ding, Huaimin Wang, Yiying Li, Hongda Jia
In this paper, we present an adaptation method of the majority of multi-agent reinforcement learning (MARL) algorithms called KnowSR which takes advantage of the differences in learning between agents.
Knowledge Distillation Multi-agent Reinforcement Learning +2
no code implementations • 27 Mar 2021 • Zijian Gao, Kele Xu, Bo Ding, Huaimin Wang, Yiying Li, Hongda Jia
In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design.
Knowledge Distillation Multi-agent Reinforcement Learning +2
no code implementations • 27 Jan 2021 • Yiying Li, Wei Zhou, Huaimin Wang, Haibo Mi, Timothy M. Hospedales
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy.
1 code implementation • NeurIPS 2020 • Wei Zhou, Yiying Li, Yongxin Yang, Huaimin Wang, Timothy M. Hospedales
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks.
no code implementations • 5 Oct 2019 • Mingyang Geng, Kele Xu, Yiying Li, Shuqi Liu, Bo Ding, Huaimin Wang
The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 28 Sep 2019 • Wei Zhou, Yiying Li
Based on experiments on the remote sensing dataset from Google Earth, our LFFN has proved effective and practical for the semantic cognition of remote sensing, achieving 89% mAP which is 4. 1% higher than that of FPN.
2 code implementations • 31 Jan 2019 • Yiying Li, Yongxin Yang, Wei Zhou, Timothy M. Hospedales
The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training.
Ranked #111 on Domain Generalization on PACS