Publications

Selected Publications: (2014-至今，其中标“*”为通讯作者，标“__”表示为本人指导研究生，目前主要为硕士研究生)

[23] Liang Dayang, Lai Jinyang, Liu Yunlong*. Intrinsic Dynamic-Driven Representation Learning for Generalization in Visual Reinforcement Learning. Under Review.

[22] Zhang Qiwei, Lin Bin, Liu Yunlong*. Safe Treatment of Sepsis: A Data Augmentation-Based Deep Reinforcement Learning Approach. Under Review.

[21] Liang Dayang, Chen Qihang, Liu Yunlong*. Sequential Action-Induced Invariant Representation for Reinforcement Learning. Neural Networks, 已录用. (SCI一区，TOP期刊, CCF B类)

[20] Yuan Linghui, Lu Xiaowei, Liu Yunlong*. Learning Task-relevant Representations via Rewards and Real Actions for Reinforcement Learning. Knowledge-Based Systems, 294, 2024. (SCI一区，TOP期刊)

[19] Liang Dayang, Zhang Yaru, Liu Yunlong*. Episodic Reinforcement Learning with Expanded State-reward Space. AAMAS 2024，2024.5(CCF B, Full/oral paper).

[18] Chen Qihang, Zhang Qiwei, Liu Yunlong*. Balancing Exploration and Exploitation in Episodic Reinforcement Learning. Expert Systems With Applications, 2023, (SCI一区, TOP期刊）.

[17] Liang Dayang, Deng Huiyi, Liu Yunlong*. The Treatment of Sepsis: An Episodic Memory-assisted Deep Reinforcement Learning Approach. Applied Intelligence，2022 (SCI 二区).

[16] Xu Aimin, Yuan Linghui, Liu Yunlong*. Sequential Decision Making with “Sequential Information” in Deep Reinforcement Learning. PRICAI 2022 (CCF C).

[15] Liu Yunlong*, Zheng Jianyang, Chang Fangfang. Learning and planning in partially observable environments without prior domain knowledge. International Journal of Approximate Reasoning, 142: 147-160, 2022. (SCI二区, CCF B类期刊)

[14] 常芳芳, 陈祺航, 刘云龙*. 局部可观测环境下未来信息辅助的无模型深度强化学习. 已录用.《南京大学学报（自然科学版）》

[13] Chen Qihang, Liang Dayang, Liu Yunlong*. Hard Negative Sample Mining for Contrastive Representation in Reinforcement Learning. The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), 2022.5. (CCF C类会议)

[12] Liang Dayang, Chen Qihang, Liu Yunlong*. Gated multi-attention representation in reinforcement learning. Knowledge-Based Systems, 2021, 233: 107535. (SCI一区，TOP期刊)

[11] 于丹宁, 倪坤, 刘云龙*. 基于循环卷积神经网络的 POMDP 值迭代算法. 计算机工程, 47(2):90-94, 2021.

[10] 倪坤, 刘云龙*, 于丹宁. 基于记忆探索策略的有模型深度强化学习算法. 微电子学与计算机, 38(4):23-28, 2021.

[9] Yu Danning, Ni Kun, Liu Yunlong*. Deep Q-Network with Predictive State Models in Partially Observable Domains. Mathematical Problems in Engineering, 2020:1-9,2020. (SCI)

[8] Liu Yunlong*, Zheng Jianyang. Online learning and planning in partially observable domains without prior knowledge. ICML 2019 Workshop on the Generative Modeling and Model-Based Reasoning for Robotics and AI, 2019. (ICML为CCF A类会议)

[7] Ni Kun, Yu Danning, Liu Yunlong*. Attention-Based Deep Q-Network in Complex Systems//International Conference on Neural Information Processing (ICONIP2019), 2019: 323-332.(CCF C类会议)

[6] Zheng Jianyang, Zhu Hexing, Chang Fangfang, Liu Yunlong*. An improved relief feature selection algorithm based on Monte-Carlo tree search. Systems Science and Control Engineering, 7(1):304-310.

[5] Huang Chunqing, An Yisheng, Zhou Sun, Hong Zhezheng, Liu Yunlong*. Basis selection in spectral learning of predictive state representations. Neurocomputing, 310:183-189, 2018. (SCI二区)

[4] Liu Yunlong*, Zeng Yifeng, Zhu Hexing, Tang Yun. Making and Improving Predictions of Interest Using an MDP Model. Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2017), 1610-1612, 2017. (CCF B类会议)

[3] Liu Yunlong*, Zhu Hexing, Zeng Yifeng, Dai Zongxiong. Learning Predictive State Representations via Monte-Carlo Tree Search//The 25th International Joint Conference on Artificial Intelligence (IJCAI-16), 3192-3198, 2016. (CCF A类会议, Full/oral paper)

[2] Liu Yunlong*, Tang Yun, Zeng Yifeng. Predictive State Representations with State Space Partitioning. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2015), 1259-1266, 2015. (CCF B类会议, Full/oral paper)

[1] Liu Yunlong, Yang Zijiang, Ji Guoli. Solving Partially Observable Problems with Inaccurate PSR Models. Information Sciences, 283:142-152, 2014. (CCF B类期刊, SCI二区)