no code implementations • 8 Oct 2019 • Zijian Gao, Amanda Kowalczyk
We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods.
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 • 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 • 14 Oct 2021 • Zijian Gao, Huanyu Liu, Jingyu Liu
The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed.
no code implementations • 10 Nov 2021 • Zijian Gao, Jingyu Liu, Weiqi Sun, Sheng Chen, Dedan Chang, Lili Zhao
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #12 on Video Retrieval on MSR-VTT-1kA (using extra training data)
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 • 24 Aug 2022 • Zijian Gao, Kele Xu, Yuanzhao Zhai, Dawei Feng, Bo Ding, XinJun Mao, Huaimin Wang
Our method involves training a self-supervised prediction model, saving snapshots of the model parameters, and using nuclear norm to evaluate the temporal inconsistency between the predictions of different snapshots as intrinsic rewards.
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 • 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.