Academic Papers
TBD (2022 Summer)

  • 1. SO(2)-EQUIVARIANT REINFORCEMENT LEARNING

  • 2. REINFORCEMENT LEARNING WITH SPARSE REWARDS USING GUIDANCE FROM OFFLINE DEMONSTRATION

  • 3. COBERL: CONTRASTIVE BERT FOR REINFORCEMENT LEARNING

  • 4. UNDERSTANDING AND PREVENTING CAPACITY LOSS IN REINFORCEMENT LEARNING

  • 5. ON LOTTERY TICKETS AND MINIMAL TASK REPRESENTATIONS IN DEEP REINFORCEMENT LEARNING

  • 6. LEARNING ALTRUISTIC BEHAVIOURS IN REINFORCEMENT LEARNING WITHOUT EXTERNAL REWARDS

  • 7. LEARNING LONG-TERM REWARD REDISTRIBUTION VIA RANDOMIZED RETURN DECOMPOSITION

  • 8. SAMPLE EFFICIENT DEEP REINFORCEMENT LEARNING VIA UNCERTAINTY ESTIMATION

  • 9. POSSIBILITY BEFORE UTILITY: LEARNING AND USING HIERARCHICAL AFFORDANCES

  • 10. TRANSFORM2ACT: LEARNING A TRANSFORM-ANDCONTROL POLICY FOR EFFICIENT AGENT DESIGN

  • 11. THE INFORMATION GEOMETRY OF UNSUPERVISED REINFORCEMENT LEARNING

  • 12. IMITATION LEARNING BY REINFORCEMENT LEARNING

  • 13. BOOSTED CURRICULUM REINFORCEMENT LEARNING

  • 14. AUTONOMOUS REINFORCEMENT LEARNING: FORMALISM AND BENCHMARKING

  • 15. LOCAL FEATURE SWAPPING FOR GENERALIZATION IN REINFORCEMENT LEARNING

  • 16. DROPOUT Q-FUNCTIONS FOR DOUBLY EFFICIENT REINFORCEMENT LEARNING

  • 17. ORCHESTRATED VALUE MAPPING FOR REINFORCEMENT LEARNING

  • 18. OFFLINE REINFORCEMENT LEARNING WITH VALUEBASED EPISODIC MEMORY

  • 19. ON-POLICY MODEL ERRORS IN REINFORCEMENT LEARNING

  • 20. HINDSIGHT FORESIGHT RELABELING FOR META-REINFORCEMENT LEARNING

  • 21. KNOW YOUR ACTION SET: LEARNING ACTION RELATIONS FOR REINFORCEMENT LEARNING

  • 22. TRUST REGION POLICY OPTIMISATION IN MULTI-AGENT REINFORCEMENT LEARNING

  • 23. LEARNING SYNTHETIC ENVIRONMENTS AND REWARD NETWORKS FOR REINFORCEMENT LEARNING

  • 24. DISTRIBUTIONAL REINFORCEMENT LEARNING WITH MONOTONIC SPLINES

  • 25. POLICY SMOOTHING FOR PROVABLY ROBUST REINFORCEMENT LEARNING

  • 26. MULTI-CRITIC ACTOR LEARNING: TEACHING RL POLICIES TO ACT WITH STYLE

  • 27. CONVERGENT AND EFFICIENT DEEP Q NETWORK ALGORITHM

  • 4월 20일 (수) 논문 세미나 – 울라 이산
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  • 4월 27일 (수) 논문 세미나 – 허주성
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  • 5월 04일 (수) 논문 세미나 – 김주봉
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  • 5월 11일 (수) 논문 세미나 – 최호빈
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  • 5월 18일 (수) 논문 세미나 – 임현교
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  • 5월 25일 (수) 논문 세미나 – 지창훈
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  • 6월 01일 (수) 논문 세미나 – 석영준
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  • 6월 08일 (수) 논문 세미나 – 최요한
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  • 6월 15일 (수) 논문 세미나 – 울라 이산
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  • 6월 22일 (수) 논문 세미나 – 허주성
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    • 08월 10일 (수) 논문 세미나 – 석영준
      Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč, "Reinforcement Learning for Solving the Vehicle Routing Problem," NeurIPS, 2018.

    • 08월 04일 (목) 논문 세미나 – 최요한
      Luiz A. Celiberto Jr., et al., "Using Transfer Learning to Speed-Up Reinforcement Learning: a Cased-Based Approach." 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting. IEEE, 2010.
      Samuel Barrett, Matthew E. Taylor, and Peter Stone, "Transfer Learning for Reinforcement Learning on a Physical Robot," AAMAS, 2010. Presentation

    • 07월 19일 (화) 논문 세미나 – 지창훈
      Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik, "Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots," MIPS, 2021.

    • 07월 13일 (수) 논문 세미나 – 최호빈
      Wang, Yihan, et al., "Dop: Off-policy multi-agent decomposed policy gradients," ICLR, 2020. Presentation

    • 07월 06일 (수) 논문 세미나 – 최요한
      Meng Fang, et al., "Curriculum-guided Hindsight Experience Replay," Advances in Neural Information Processing Systems 32, 2019. Presentation

    • 06월 16일 (목) 논문 세미나 – 석영준
      Yang Yang, Yulin Hu, M. Cenk Gursoy, "Deep Reinforcement Learning and Optimization Based Green Mobile Edge Computing ," IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), 2021.
      Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto, "Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning," arXiv:2107.09645, 2022.

    • 06월 8일 (수) 논문 세미나 – 울라 이산
      Youn J, Han Y-H, "Intelligent Task Dispatching and Scheduling Using a Deep Q-Network in a Cluster Edge Computing System," Sensors, 2022.

    • 06월 3일 (금) 논문 세미나 – 최호빈
      Hyeoksoo Lee, Jiwoo Hong, and Jongpil Jeong, "MARL-Based Dual Reward Model on Segmented Actions for Multiple Mobile Robots in AutomatedWarehouse Environment," Applied Sciences, 2022. Presentation

    • 05월 27일 (금) 논문 세미나 – 장용연
      Alexander C. Li, Lerrel Pinto, Pieter Abbeel, "Generalized Hindsight for Reinforcement Learning," arXiv, 2020. Presentation

    • 05월 18일 (수) 논문 세미나 – 허주성
      Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher, "The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning," https://arxiv.org/abs/2108.02755.

    • 05월 4일 (수) 논문 세미나 – 지창훈, 김주봉
      1) Aravind Srinivas, et al., "CURL: Contrastive Unsupervised Representations for Reinforcement Learning," arXiv, 2020. Presentation
      2) Yuri Burda, et al., "Exploration by Random Network Distillation," ICLR, 2019.

    • 04월 27일 (수) 논문 세미나 – 김주봉, 장용연
      1) New Paper Idea
      2) Marcin Andrychowicz, et al., "Hindsight Experience Replay," NIPS, 2017.

    • 04월 20일 (수) 논문 세미나 – 울라 이산 (외부 세미나)
      A. Qadeer and M. J. Lee, "DDPG-Edge-Cloud: A Deep-Deterministic Policy Gradient based Multi-Resource Allocation in Edge-Cloud System," 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2022.

    • 04월 13일 (수) 논문 세미나 – 지창훈
      Nicklas Hansen, Xiaolong Wang, et al. "Temporal Difference Learning for Model Predictive Control." arXiv, 2022. Presentation

    • 04월 06일 (수) 논문 세미나 – 석영준
      Sur, Giwon, et al. "A Deep Reinforcement Learning-Based Scheme for Solving Multiple Knapsack Problems." Applied Sciences 12.6 (2022): 3068. Presentation

    • 03월 30일 (수) 논문 세미나 – 최요한
      Yeo Jin Kim, Min Chi, “Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning,” arXiv, 2021. Presentation

    • 03월 23일 (수) 논문 세미나 – 임현교
      Refaei Afshar, R., Zhang, Y., Firat, M., and Kaymak, U., “A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning,” arXiv, 2020. Presentation

    • 03월 16일 (수) 논문 세미나 – 최호빈
      Chenghao Li, et al., "Celebrating Diversity in Shared Multi-Agent Reinforcement Learning," NIPS, 2021. Presentation

    • 03월 10일 (목) 논문 세미나 – 허주성
      Stephan Zheng, et al., "The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies," https://arxiv.org/abs/2004.13332.

    • 03월 03일 (목) 논문 세미나 – 김주봉
      Irwan Bello, et al., "Neural Combinatorial Optimization with Reinforcement Learning,", ICLR, 2017.
      Thomas D. Barrett, et al., "Exploratory Combinatorial Optimization with Reinforcement Learning,", AAAI, 2020.
      Ofir Nachum, et al., "Data-Efficient Hierarchical Reinforcement Learning,", NIPS, 2018.

    • 02월 24일 (목) 논문 세미나 – 최요한
      Marcin Andrychowicz, et al., "Hindsight Experience Replay," NIPS, 2017. Presentation

    • 02월 15일 (화) 논문 세미나 – 울라 이산
      F. Qi, L. Zhuo and C. Xin, "Deep Reinforcement Learning Based Task Scheduling in Edge Computing Networks," 2020 IEEE/CIC International Conference on Communications in China (ICCC), pp. 835-840, 2020. Presentation

    • 02월 08일 (화) 논문 세미나 – 석영준
      RL — Policy Gradient Explained (Part I & II) Presentation

    • 01월 25일 (화) 논문 세미나 – 임현교
      H. Huang et al., "Scalable Orchestration of Service Function Chains in NFV-Enabled Networks: A Federated Reinforcement Learning Approach," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2558-2571, Aug. 2021. Presentation

    • 01월 18일 (화) 논문 세미나 – 지창훈
      Weirui Ye, et al., "Mastering Atari Games with Limited Data," NIPS, 2021.

    • 01월 04일 (화) 논문 세미나 – 최요한
      Junhyuk Oh, Satinder Singh, and Honglak Lee, "Value Prediction Network," NIPS, 2017. Presentation
    • 12월 28일 (화) 논문 세미나 – 울라 이산
      Taihui Li, et al., "An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network ," IEEE Access, July 2020.

    • 12월 21일 (화) 논문 세미나 – 최호빈
      Gupta, Tarun, et al., "Uneven: Universal value exploration for multi-agent reinforcement learning," International Conference on Machine Learning. PMLR, 2021.

    • 12월 14일 (화) 논문 세미나 – 지창훈
      Cameron Browne et al., "A Survey of Monte Carlo Tree Search Methods," IEEE Transactions on Computational Intelligence and AI in Games, Vol. 4, No. 1, March 2012.

    • 12월 07일 (화) 논문 세미나 – 김주봉
      Woojun Kim, Jongeui Park, Youngchul Sung, "Communication in Multi-Agent Reinforcement Learning-Intention Sharing," ICLR, 2021.

    • 11월 30일 (화) 논문 세미나 – 지창훈
      Levine, Sergey, et al., "Offline reinforcement learning: Tutorial, review, and perspectives on open problems.", arXiv preprint arXiv, 2020

    • 11월 23일 (화) 논문 세미나 – 울라 이산
      Lu Zhang, et al., "Task Offloading and Trajectory Control for UAV-Assisted Mobile Edge Computing Using Deep Reinforcement Learning," IEEE ACCESS, 2021.

    • 11월 16일 (화) 논문 세미나 – 최요한
      Marc G. Bellemare, Will Dabney, and Remi Munos, "A Distributional Perspective on Reinforcement Learning," ICML, 2017.

    • 10월 26일 (화) 논문 세미나 – 최호빈
      Lowe, Ryan, et al., "Multi-agent actor-critic for mixed cooperative-competitive environments," NIPS, 2017.

    • 10월 19일 (화) 논문 세미나 – 김주봉
      Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, Chongjie Zhang, "QPLEX: DUPLEX DUELING MULTI-AGENT Q-LEARNING," ICLR, 2021.

    • 10월 12일 (화) 논문 세미나 – 지창훈
      Richard S. Sutton. “Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming.” Machine learning proceedings 1990. Morgan Kaufmann, 1990.
      Richard S. Sutton. “Dyna, an Integrated Architecture for Learning, Planning, and Reacting.” ACM Sigart Bulletin, 1991.

    • 10월 05일 (화) 논문 세미나 – 울라 이산
      P. Zhang, et al., "Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning," IEEE Internet of Things Journal, 2021.

    • 09월 28일 (화) 논문 세미나 – 최요한
      Hanjun Dai, et al., "Learning Combinatorial Optimization Algorithms over Graphs," NIPS, 2017.

    • 09월 14일 (화) 논문 세미나 – 최요한
      Ziyu Wang, et al., "Dueling Network Architectures for Deep Reinforcement Learning." International Conference on Machine Learning. PMLR, 2016.

    • 09월 07일 (화) 논문 세미나 – 김주봉
      Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games," arXiv preprint arXiv:2103.01955, 2021.

    • 08월 30일 (월) 논문 세미나 – 지창훈
      Hamid Ali, Hammad Majeed, Imran Usman, Khaled A. Almejalli. “Reducing Entropy Overestimation in Soft Actor Critic Using Dual Policy Network.” Hindawi, 2021.

    • 08월 23일 (월) 논문 세미나 – 울라 이산
      Yi, Mengjie, Xijun Wang, Juan Liu, Yan Zhang and B. Bai. “Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks.” IEEE INFOCOM, pp. 716-721, 2020.

    • 08월 09일 (월) 논문 세미나 – 임현교
      N. Navid, F. Hung, S. Soleyman and D. Khosla. “Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning.” ArXiv abs/2010.04740, 2020.

    • 08월 02일 (월) 논문 세미나 – 김주봉
      T. Wang, T. Gupta, A. Mahajan, B. Peng, S. Whiteson, C. Zhang, "RODE: LEARNING ROLES TO DECOMPOSE MULTI-AGENT TASKS," arXiv preprint arXiv:2010.01523, 2020.

    • 07월 26일 (월) 논문 세미나 – 울라 이산
      Omar Bouhamed et. al., "A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling," IEEE Access, 2020.

    • 07월 19일 (월) 논문 세미나 – 최호빈
      Huang, Shengyi, and Santiago Ontañón, "A closer look at invalid action masking in policy gradient algorithms," arXiv preprint arXiv:2006.14171, 2020.

    • 07월 12일 (월) 논문 세미나 – 지창훈
      Kulkarni, Tejas D., et al., "Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation," Advances in neural information processing systems, 2016.

    • 06월 28일 (월) 논문 세미나 – 최요한
      Richard S. Sutton, et. al., "Policy Gradient Methods for Reinforcement Learning with Function Approximation," NIPS, 1999.

    • 06월 21일 (월) 논문 세미나 – 지창훈
      Pathak, Deepak, et al., "Curiosity-driven exploration by self-supervised prediction," International Conference on Machine Learning. PMLR, 2017.

    • 06월 14일 (월) 논문 세미나 – 임현교
      A. Rkhami, et al., "On the Use of Graph Neural Networks for Virtual Network Embedding," 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020.

    • 05월 31일 (월) 논문 세미나 – 김주봉
      Qiang Ma, et al., "Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning," arXiv:1911.04936, 2019.
      Vaswani A., et al., "Attention Is All You Need," NIPS, 2017.
      Vinyals, Oriol and Fortunato, Meire and Jaitly, Navdeep, "Pointer Networks," NIPS, 2015.

    • 05월 24일 (월) 논문 세미나 – 최호빈
      Tang, Hengliang, et al., "A novel hierarchical soft actor-critic algorithm for multi-logistics robots task allocation," IEEE Access, 2021.

    • 05월 20일 (목) 논문 세미나 – 임현교
      Wang, Cong, et al. "Modeling on Virtual Network Embedding Using Reinforcement Learning," Concurrency and Computation: Practice and Experience, 2020.
      H. Yao, S. Ma, J. Wang, P. Zhang, C. Jiang and S. Guo, "A Continuous-Decision Virtual Network Embedding Scheme Relying on Reinforcement Learning," in IEEE Transactions on Network and Service Management, 2020.

    • 05월 12일 (수) 논문 세미나 – 지창훈
      Junta Wu and Huiyun Li, "Deep Ensemble Reinforcement Learning with Multiple DeepDeterministic Policy Gradient Algorithm" Hindawi, 2020.

    • 05월 03일 (월) 논문 세미나 – 김주봉
      Shariq Iqbal, et al., "RANDOMIZED ENTITY-WISE FACTORIZATION FOR MULTI-AGENT REINFORCEMENT LEARNING," arXiv:2006.04222, 2020.

    • 04월 05일 (월) 논문 세미나 – 임현교
      H. Yao, X. Chen, P. Zhang and L. Wang, "A novel reinforcement learning algorithm for virtual network embedding," Neurocomputing, 2018.

    • 03월 22일 (월) 논문 세미나 – 김주봉
      Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson, "Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning," NIPS, 2020.

    • 03월 15일 (월) 논문 세미나 – 최호빈
      Christianos, Filippos, Lukas Schäfer, and Stefano V. Albrecht, "Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning," arXiv preprint arXiv:2006.07169, 2020.

    • 03월 08일 (월) 논문 세미나 – 지창훈
      Scott Fujimoto, et al., "Addressing Function Approximation Error in Actor-Critic Methods", International Conference on Machine Learning, 2018.

    • 03월 02일 (화) 논문 세미나 – 임현교
      Z. Yan, et al., "Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks," IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol. 38, No. 6, June, 2020.

    • 02월 08일 (월) 논문 세미나 – 울라 이산
      Khoa Nguyen, et al., "Efficient Virtual Network Embedding with Node Ranking and Intelligent Link Mapping," IEEE 9th International Conference on Cloud Networking (CloudNet), November, 2020.

    • 02월 01일 (월) 논문 세미나 – 김주봉
      Yong Liu, et al., "Multi-Agent Game Abstraction via Graph Attention Neural Network," AAAI, 2020.

    • 01월 25일 (월) 논문 세미나 – 최호빈
      Enright, John J., and Peter R. Wurman, "Optimization and coordinated autonomy in mobile fulfillment systems," Workshops at the twenty-fifth AAAI conference on artificial intelligence, 2011.

    • 01월 27일 (수) 논문 세미나 – 울라 이산
      Min Feng, et al., "Virtual Network Embedding based on Modified Genetic Algorithm," Peer-to-Peer Networking and Applications, October 2019.

    • 01월 18일 (월) 논문 세미나 – 지창훈
      Mohammed Hossny, et al., "Refined Continuous Control of DDPG Actors via Parametrised Activation," arXiv:2006.02818, June 2020.

    • 01월 11일 (월) 논문 세미나 – 임현교
      Mosharaf Chowdhury, et al., "ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping," IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 20, NO. 1, FEBRUARY 2012.

    • 01월 04일 (월) 논문 세미나 – 황규영
      Tuomas Haarnoja, et al., "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor," ICML 2018.
    • 12월 21일 (화) 논문 세미나 – 최호빈, 김주봉
      Anuj Mahajan, et al., "MAVEN: Multi-Agent Variational Exploration," NeurIPS, 7611-7622, 2019.

    • 12월 14일 (월) 논문 세미나 – 울라 이산
      M. Feng, et al., "Topology-Aware Virtual Network Embedding Through the Degree," National Doctoral Academic Forum on Information and Communications Technology 2013, Aug, 2013.

    • 11월 16일 (월) 논문 세미나 – 임현교
      Fengsheng Wei et al., “Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning with Large Action Space,” IEEE Transactions on Network and Service Management, 2020.

    • 11월 09일 (월) 논문 세미나 – 김주봉
      Kyunghwan Son et al., “QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement learning,” Proceedings of the 36th International Conference on Machine Learning, 2019.

    • 11월 02일 (월) 논문 세미나 – 울라 이산
      Haozhe Wanga et al., “Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach,” Information Sciences, 498, pp. 106-116, 2019.

    • 10월 19일 (월) 논문 세미나 – 지창훈
      S. Vassilaras et al., “Applying Deep Learning and Reinforcement Learning to Traveling Salesman Problem” in IEEE International Conference on Systems, Man, and Cybernetics(SMC). Aug .2018.

    • 10월 05일 (월) 논문 세미나 – 임현교
      S. Vassilaras et al., “The Algorithmic Aspects of Network Slicing,” in IEEE Communications Magazine, vol. 55, no. 8, pp. 112-119, Aug. 2017.

    • 09월 28일 (월) 논문 세미나 – 김주봉
      Yuki Miyashita and Toshiharu Sugawara, “Analysis of coordinated behavior structures with multi-agent deep reinforcement learning,”, Applied Intelligence, 2020.

    • 09월 21일 (월) 논문 세미나 – 지창훈
      Galina L. Rogova, Jyotsna Kasturi, "Reinforcement Learning Neural Network For Distributed Decision Making",2002

    • 09월 14일 (월) 논문 세미나 – 임현교
      J. Du, X. Huang, F. Wu and S. Leng, "Reinforcement Learning Empowered QoS-aware Adaptive Q-Routing in Ad-hoc Networks," 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, pp. 551-556, 2020.

    • 09월 07일 (월) 논문 세미나 – 최호빈
      Yali Du, Lei Han, Meng Fang, Tianhong Dai, Ji Liu, Dacheng Tao, "LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning," NIPS, 2019.

    • 07월 30일 (목) 논문 세미나 – 임현교
      Q. Fu, E. Sun, K. Meng, M. Li and Y. Zhang, "Deep Q-Learning for Routing Schemes in SDN-Based Data Center Networks," in IEEE Access, vol. 8, pp. 103491-103499, 2020.

    • 07월 23일 (목) 논문 세미나 – 황규영
      Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy, “Deep Exploration via Bootstrapped DQN,” Advances in Neural Information Processing Systems 29 (NIPS 2016).

    • 02월 17일 (월) 논문 세미나 – 최호빈
      Zäzilia Seibold, Thomas Stoll, Kai Furmans, “Layout-optimized sorting of goods with decentralized controlled conveying modules,” 2013 IEEE International Systems Conference (SysCon), Apr. 2013.

    • 02월 11일 (화) 논문 세미나 – 김주봉
      T. Eccles et al., “Biases for Emergent Communication in Multi-agent Reinforcement Learning,”, NIPS, 2019.

    • 02월 03일 (월) 교재 세미나 – 황규영
      Richard S. Sutton and Andrew G. Barto, “Reinforcement Learning: An Introduction,” second edition, MIT Press, Cambridge, MA, 2018.

    • 01월 20일 (월) 논문 세미나 – 임현교


    • 01월 17일 (금) 논문 세미나 – 황규영
      Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver, “Rainbow: Combining Improvements in Deep Reinforcement Learning,” AAAI, 2018.

    • 01월 06일 (월) 논문 세미나 – 최호빈


  • 05월 27일 (월) 논문 세미나 – 임현교

  • 05월 13, 20일 (월) 논문 세미나 – 권도형

  • 05월 08일 (수) 논문 세미나 – 황규영
    François Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions”, arXiv:1610.02357v3 [cs.CV] 4 Apr 2017.

  • 04월 29일 (월) 논문 세미나 – 최호빈

  • 04월 08일 (월) 논문 세미나 – 임현교

  • 04월 01일 (월) 논문 세미나 – 허주성

  • 03월 25일 (월) 논문 세미나 – 최호빈


  • 03월 18일 (월) 논문 세미나 – 황규영
    Sartoretti, Y. Wu, W. Paivine, T. K. S. Kumar, S. Koenig, and H. Choset, “Distributed Reinforcement Learning for MultiRobot Decentralized Collective Construction”, DARS 2018.

  • 03월 11일 (월) 논문 세미나 – 임현교

  • 03월 4일 (월) 논문 세미나 – 김주봉

  • 02월 25일 (월) 논문 세미나 – 김주봉

  • 02월 15일 (금) 논문 세미나 – 권도형

  • 02월 07일 (목) 논문 세미나 – 임현교

  • 01월 31일 (목) 논문 세미나 – 김주봉


    • 12월 20일 (목) 논문 세미나 – 임현교
      M. Spryn, A. Sharma, D. Parkar, and M. Shrimal, "Distributed Deep Reinforcement Learning on the Cloud for Autonomous Driving," ACM/IEEE 1st International Workshop on Software Engineering for AI in Autonomous Systems, 2018.

    • UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning ICML 2021

    • Communication in Multi-Agent Reinforcement Learning-Intention Sharing ICLR 2021

    • Succinct and Robust Multi-Agent Communication With Temporal Message Control NIPS 2020

    • Multi-agent active perception with prediction rewards NIPS 2020

    • Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning NIPS 2020

    • Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity NIPS 2020

    • Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning NIPS 2020

    • Learning Multi-Agent Communication through Structured Attentive Reasoning NIPS 2020

    • Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning NIPS 2020

    • Robust Multi-Agent Reinforcement Learning with Model Uncertainty NIPS 2020

    • Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning NIPS 2020

    • Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward NIPS 2020

    • Contextual Games: Multi-Agent Learning with Side Information NIPS 2020

    • Learning Individually Inferred Communication for Multi-Agent Cooperation NIPS 2020

    • EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning NIPS 2020

    • Joint Policy Search for Multi-agent Collaboration with Imperfect Information NIPS 2020

    • "ATTENTION, LEARN TO SOLVE ROUTING PROBLEMS!" in conference paper at ICLR 2019

    • "DeepViNE: Virtual Network Embedding with Deep Reinforcement Learning," IEEE INFOCOM 2019.
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