1 code implementation • 27 Jul 2023 • Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, Ding Zhao, Liangjun Zhang, Hesheng Wang, Wei Tsang Ooi, Ruijie Zhu, Ziyang Song, Li Liu, Tianzhu Zhang, Jun Yu, Mohan Jing, Pengwei Li, Xiaohua Qi, Cheng Jin, Yingfeng Chen, Jie Hou, Jie Zhang, Zhen Kan, Qiang Ling, Liang Peng, Minglei Li, Di Xu, Changpeng Yang, Yuanqi Yao, Gang Wu, Jian Kuai, Xianming Liu, Junjun Jiang, Jiamian Huang, Baojun Li, Jiale Chen, Shuang Zhang, Sun Ao, Zhenyu Li, Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
no code implementations • 18 Feb 2023 • Jinming Ma, Feng Wu, Yingfeng Chen, Xianpeng Ji, Yu Ding
Specifically, we observe that these issues make conventional RL methods difficult to learn a useful state representation in the end-to-end training with multimodal information.
no code implementations • 7 Feb 2023 • Rundong Wang, Longtao Zheng, Wei Qiu, Bowei He, Bo An, Zinovi Rabinovich, Yujing Hu, Yingfeng Chen, Tangjie Lv, Changjie Fan
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 27 Jan 2023 • Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao
Generally, episodic control-based approaches are solutions that leverage highly-rewarded past experiences to improve sample efficiency of DRL algorithms.
no code implementations • 4 Oct 2022 • Anjun Chen, Xiangyu Wang, Kun Shi, Shaohao Zhu, Bin Fang, Yingfeng Chen, Jiming Chen, Yuchi Huo, Qi Ye
However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images.
no code implementations • 2 Oct 2022 • Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Jinyi Liu, Yingfeng Chen, Changjie Fan
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks.
no code implementations • 29 Jul 2022 • Yixiang Wang, Yujing Hu, Feng Wu, Yingfeng Chen
In this paper, we propose to automatically generate goal-consistent intrinsic rewards for the agent to learn, by maximizing which the expected accumulative extrinsic rewards can be maximized.
no code implementations • 22 Jul 2022 • Yunlong Ran, Jing Zeng, Shibo He, Lincheng Li, Yingfeng Chen, Gimhee Lee, Jiming Chen, Qi Ye
In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one.
no code implementations • 27 May 2022 • Wei Qiu, Weixun Wang, Rundong Wang, Bo An, Yujing Hu, Svetlana Obraztsova, Zinovi Rabinovich, Jianye Hao, Yingfeng Chen, Changjie Fan
During execution durations, the environment changes are influenced by, but not synchronised with, action execution.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • NeurIPS 2021 • Xiangyu Liu, Hangtian Jia, Ying Wen, Yaodong Yang, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
2 code implementations • NeurIPS 2021 • Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie Zhang
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
no code implementations • 29 Sep 2021 • Xiao Liu, Meng Wang, Zhaorong Wang, Yingfeng Chen, Yujing Hu, Changjie Fan, Chongjie Zhang
Imitation learning is one of the methods for reproducing expert demonstrations adaptively by learning a mapping between observations and actions.
no code implementations • 16 Aug 2021 • Zhao-Hua Li, Yang Yu, Yingfeng Chen, Ke Chen, Zhipeng Hu, Changjie Fan
The empirical results show that the proposed method can preserve a higher cumulative reward than behavior cloning and learn a more consistent policy to the original one.
1 code implementation • 19 Jun 2021 • Ke Chen, Yufei Li, Yingfeng Chen, Changjie Fan, Zhipeng Hu, Wei Yang
We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99. 5\% recall in detecting non-crashing bugs such as game GUI glitches.
no code implementations • 9 Jun 2021 • Xiangyu Liu, Hangtian Jia, Ying Wen, Yaodong Yang, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
no code implementations • 1 Jan 2021 • Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
no code implementations • 6 Dec 2020 • Hangtian Jia, Yujing Hu, Yingfeng Chen, Chunxu Ren, Tangjie Lv, Changjie Fan, Chongjie Zhang
We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game.
no code implementations • NeurIPS 2020 • Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, Changjie Fan
In this paper, we consider the problem of adaptively utilizing a given shaping reward function.
no code implementations • 28 Sep 2020 • Tianpei Yang, Jianye Hao, Weixun Wang, Hongyao Tang, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yujing Hu, Yingfeng Chen, Changjie Fan
In many cases, each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
Open-Ended Question Answering Reinforcement Learning (RL) +1
1 code implementation • 15 Jun 2020 • Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
2 code implementations • 10 Mar 2020 • Yan Song, Yingfeng Chen, Yujing Hu, Changjie Fan
In this paper, we focus on improving the effectiveness of finding unknown states and propose action balance exploration, which balances the frequency of selecting each action at a given state and can be treated as an extension of upper confidence bound (UCB) to deep reinforcement learning.
no code implementations • 6 Sep 2019 • Weixun Wang, Tianpei Yang, Yong liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao
In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.
1 code implementation • 5 Sep 2019 • Yu Chen, Yingfeng Chen, Zhipeng Hu, Tianpei Yang, Changjie Fan, Yang Yu, Jianye Hao
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning.
1 code implementation • ICLR 2020 • Weixun Wang, Tianpei Yang, Yong liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao
ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.
no code implementations • 31 May 2019 • Wen-Ji Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou
Experience reuse is key to sample-efficient reinforcement learning.
1 code implementation • 12 Mar 2019 • Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, Changjie Fan
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces.
no code implementations • 25 Sep 2018 • Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Zhaopeng Meng, Changjie Fan, Li Wang
Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning.