Hao, Jianye

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain

Hao, J., Yang, T., Tang, H., Bai, C., Liu, J., Meng, Z., Liu, P., & Wang, Z. (2021) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 192

GUI Agents with Foundation Models: A Comprehensive Survey

Wang, S., Liu, W., Chen, J., Zhou, Y., Gan, W., Zeng, X., Che, Y., Yu, S., Hao, X., Shao, K., et al. (2024) · arXiv

Web Data Mining and AnalysisMultimodal Machine Learning Applications

Cited by 32

DeepRisk: a deep learning approach for genome-wide assessment of common disease risk

Peng, J., Bao, Z., Li, J., Han, R., Wang, Y., Han, L., Peng, J., Wang, T., Hao, J., Wei, Z., et al. (2024) · Fundamental Research

Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestock

Cited by 15

Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning

Wu, Z., Yu, C., Chen, C., Hao, J., & Zhuo, H. H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 12

EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems

Dehghan, M., Alomrani, M., Bagga, S., Alfonso-Hermelo, D., Bibi, K., Ghaddar, A., Zhang, Y., Li, X., Hao, J., Liu, Q., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 12

CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis

Xiao, Y., Liu, J., Zheng, Y., Xie, X., Hao, J., Li, M., Wang, R., Ni, F., Li, Y., Luo, J., et al. (2024) · arXiv

Single-cell and spatial transcriptomicsScientific Computing and Data Management

Cited by 8

MFE-ETP: A Comprehensive Evaluation Benchmark for Multi-modal Foundation Models on Embodied Task Planning

Zhang, M., Fu, X., Hao, J., Han, P., Zhang, H., Shi, L., Tang, H., & Zheng, Y. (2024) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 6

Deep Research Agents: A Systematic Examination And Roadmap

Huang, Y., Chen, Y., Zhang, H., Li, K., Zhou, H., Fang, M., Yang, L., Li, X., Shang, L., Xu, S., et al. (2025) · arXiv

AI-based Problem Solving and PlanningInformation Retrieval and Search Behavior

Cited by 3

Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning

Christianos, F., Papoudakis, G., Zimmer, M., Coste, T., Wu, Z., Chen, J., Khandelwal, K., Doran, J., Feng, X., Liu, J., et al. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 2

K^2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control

Wu, Z., Mo, D., Lu, H., Xing, J., Liu, J., Jing, Y., Li, K., Shao, K., Hao, J., & Shi, Y. (2026) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 0