1 code implementation • 13 Nov 2023 • Zhenyu Ren, Guoliang Li, Chenqing Ji, Chao Yu, Shuai Wang, Rui Wang
In the proposed CASTER simulator, however, the training dataset can be simulated via existing videos.
no code implementations • 3 Nov 2023 • Chao Yu, Yan Luo, Renqi Chen, Rui Wang
In this letter, a cooperative sensing framework based on millimeter wave (mmWave) communication systems is proposed to detect tiny motions with a millimeter-level resolution.
no code implementations • 1 Nov 2023 • Xinyi Yang, Yuxiang Yang, Chao Yu, Jiayu Chen, Jingchen Yu, Haibing Ren, Huazhong Yang, Yu Wang
In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks.
no code implementations • 29 Oct 2023 • Zelai Xu, Chao Yu, Fei Fang, Yu Wang, Yi Wu
Agents built with large language models (LLMs) have recently achieved great advancements.
no code implementations • 7 Oct 2023 • Jiayu Chen, Zelai Xu, Yunfei Li, Chao Yu, Jiaming Song, Huazhong Yang, Fei Fang, Yu Wang, Yi Wu
In this work, we present a novel subgame curriculum learning framework for zero-sum games.
no code implementations • 5 Oct 2023 • Zelai Xu, Yancheng Liang, Chao Yu, Yu Wang, Yi Wu
Alternatively, Policy-Space Response Oracles (PSRO) is an iterative framework for learning NE, where the best responses w. r. t.
no code implementations • 22 Sep 2023 • Botian Xu, Feng Gao, Chao Yu, Ruize Zhang, Yi Wu, Yu Wang
In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim.
no code implementations • 4 Sep 2023 • Wen Liang, Chao Yu, Brian Whiteaker, Inyoung Huh, Hua Shao, Youzhi Liang
In the past few years, AlphaZero's exceptional capability in mastering intricate board games has garnered considerable interest.
1 code implementation • 27 Jun 2023 • Weihua Du, Jinglun Zhao, Chao Yu, Xingcheng Yao, Zimeng Song, Siyang Wu, Ruifeng Luo, Zhiyuan Liu, Xianzhong Zhao, Yi Wu
Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training.
no code implementations • 21 Jun 2023 • Chao Yu, Wenhao Zhu, Chaoming Liu, XiaoYu Zhang, Qiuhong zhai
This indicates that different downstream tasks have different levels of sensitivity to sentence components.
no code implementations • 14 Jun 2023 • Xuechen Mu, Hankz Hankui Zhuo, Chen Chen, Kai Zhang, Chao Yu, Jianye Hao
Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
1 code implementation • 1 Jun 2023 • Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang
Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL), research on safe RL has made significant progress in recent years.
1 code implementation • International Conference on Autonomous Agents and Multiagent Systems 2023 • Yucong Zhang, Chao Yu
Recently, Multi-Agent Reinforcement Learning (MARL) has been applied to a large number of scenarios and has shown promising performance.
no code implementations • 24 Apr 2023 • Chao Yu, Xuejing Zheng, Hankz Hankui Zhuo, Hai Wan, Weilin Luo
Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.
no code implementations • 22 Feb 2023 • Chao Yu, Yifei Sun, Yan Luo, Rui Wang
It is demonstrated via experiments that the mmAlert system can always detect the motions of the walking person close to the LoS path, and predict 90\% of the LoS blockage with sensing time of 1. 4 seconds.
no code implementations • 8 Feb 2023 • Xinyi Yang, Shiyu Huang, Yiwen Sun, Yuxiang Yang, Chao Yu, Wei-Wei Tu, Huazhong Yang, Yu Wang
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
1 code implementation • 3 Feb 2023 • Chao Yu, Jiaxuan Gao, Weilin Liu, Botian Xu, Hao Tang, Jiaqi Yang, Yu Wang, Yi Wu
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
1 code implementation • 20 Jan 2023 • Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo
In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples.
1 code implementation • 9 Jan 2023 • Chao Yu, Xinyi Yang, Jiaxuan Gao, Jiayu Chen, Yunfei Li, Jijia Liu, Yunfei Xiang, Ruixin Huang, Huazhong Yang, Yi Wu, Yu Wang
Simply waiting for every robot being ready for the next action can be particularly time-inefficient.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 28 Nov 2022 • Wenxuan Zhu, Chao Yu, Qiang Zhang
Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world.
no code implementations • 15 Jun 2022 • Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu
Despite all the advantages, we revisit these two principles and show that in certain scenarios, e. g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
1 code implementation • 3 Apr 2022 • Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, Wei Chu
In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion.
no code implementations • 2 Apr 2022 • Chao Yu, Yi Shen, Yue Mao, Longjun Cai
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy.
no code implementations • 28 Mar 2022 • Jie Li, Chao Yu, Yan Luo, Yifei Sun, Rui Wang
Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
no code implementations • 18 Dec 2021 • Hankz Hankui Zhuo, Shuting Deng, Mu Jin, Zhihao Ma, Kebing Jin, Chen Chen, Chao Yu
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i. e., data efficiency, lack of the interpretability and transferability.
no code implementations • 12 Dec 2021 • Weilin Liu, Ye Mu, Chao Yu, Xuefei Ning, Zhong Cao, Yi Wu, Shuang Liang, Huazhong Yang, Yu Wang
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
no code implementations • 18 Nov 2021 • Xuejing Zheng, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo
In this paper, we propose Lifelong reinforcement learning with Sequential linear temporal logic formulas and Reward Machines (LSRM), which enables an agent to leverage previously learned knowledge to fasten learning of logically specified tasks.
1 code implementation • NeurIPS 2021 • Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, Haiyin Piao, Hankz Hankui Zhuo
We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm that extends the original Proximal Policy Optimization (PPO) to the multi-agent setting.
no code implementations • 12 Oct 2021 • Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, Yi Wu
In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.
no code implementations • 9 May 2021 • Sihang Chen, Weiqi Luo, Chao Yu
In recent years, quantitative investment methods combined with artificial intelligence have attracted more and more attention from investors and researchers.
no code implementations • 10 Mar 2021 • Zheng-Ping Li, Jun-Tian Ye, Xin Huang, Peng-Yu Jiang, Yuan Cao, Yu Hong, Chao Yu, Jun Zhang, Qiang Zhang, Cheng-Zhi Peng, Feihu Xu, Jian-Wei Pan
Long-range active imaging has widespread applications in remote sensing and target recognition.
2 code implementations • ICLR 2021 • Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Du, Yu Wang, Yi Wu
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.
12 code implementations • 2 Mar 2021 • Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, Yi Wu
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
no code implementations • 4 Jan 2021 • Yue Mao, Yi Shen, Chao Yu, Longjun Cai
Some recent work focused on solving a combination of two subtasks, e. g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely.
Aspect-oriented Opinion Extraction
Aspect Sentiment Triplet Extraction
+4
no code implementations • 1 Jan 2021 • Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu
We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.
no code implementations • 24 May 2020 • Wenwu Xie, Jian Xiao, Jinxia Yang, Xin Peng, Chao Yu, Peng Zhu
Since the signal with strong power should be demodulated first for successive interference cancellation (SIC) demodulation in non-orthogonal multiple access (NOMA) systems, the base station (BS) should inform the near user terminal (UT), which has allocated higher power, of modulation mode of the far user terminal.
no code implementations • 10 Nov 2019 • Chao Yu, Zhiguo Su
In this paper, a novel neural network activation function, called Symmetrical Gaussian Error Linear Unit (SGELU), is proposed to obtain high performance.
no code implementations • 22 Aug 2019 • Chao Yu, Jiming Liu, Shamim Nemati
As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback.
no code implementations • 10 Nov 2018 • Chao Yu, Tianpei Yang, Wenxuan Zhu, Dongxu Wang, Guangliang Li
Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning.
no code implementations • 9 Nov 2018 • Chao Yu
We then propose a hierarchical supervision framework to explicitly model the PoG, and define step by step how to realize the core principle of the framework and compute the optimal PoG for a control problem.
2 code implementations • 22 Sep 2018 • Chao Yu, Zuxin Liu, Xinjun Liu, Fugui Xie, Yi Yang, Qi Wei, Qiao Fei
It is one of the state-of-the-art SLAM systems in high-dynamic environments.
Robotics