顶会论文 || 65篇"IJCAI"深度强化学习论文汇总

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1. A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer: Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Xu Sun, Zhifang Sui

2. A Restart-based Rank-1 Evolution Strategy for Reinforcement Learning: Zefeng Chen, Yuren Zhou, Xiao-yu He, Siyu Jiang

3. An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments:Elaheh Barati, Xuewen Chen

4. An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents: Felipe Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman

5. Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards: Zhao-Yang Fu, De-Chuan Zhan, Xin-Chun Li, Yi-Xing Lu

6. Autoregressive Policies for Continuous Control Deep Reinforcement Learning:Dmytro Korenkevych, Ashique Rupam Mahmood, Gautham Vasan, James Bergstra

7. Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces :Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, Changjie Fan

8. Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Network Representation:Wei Qiu, Haipeng Chen, Bo An

9. Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning: Zhenshan Bing, Christian Lemke, Zhuangyi Jiang, Kai Huang, Alois Knoll

10. Explaining Reinforcement Learning to Mere Mortals: An Empirical Study: Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett

11. Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space: Zhou Fan, Rui Su, Weinan Zhang, Yong Yu

12. Incremental Learning of Planning Actions in Model-Based Reinforcement Learning: Alvin Ng, Ron Petrick

13. Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration: Zhaodong Wang, Matt Taylor

14. Interactive Teaching Algorithms for Inverse Reinforcement Learning: Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, Adish Singla

15. Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning: Yaodong Yang, Jianye Hao, Yan Zheng, Chao Yu

16. Meta Reinforcement Learning with Task Embedding and Shared Policy: Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang

17. Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control: Kenny Young, Baoxiang Wang, Matthew E. Taylor

18. Playing Card-Based RTS Games with Deep Reinforcement Learning: Tianyu Liu, Zijie Zheng, Hongchang Li, Kaigui Bian, Lingyang Song

19. Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning: Shihong Song, Jiayi Weng, Hang Su, Dong Yan, Haosheng Zou, Jun Zhu

20. Reinforcement Learning Experience Reuse with Policy Residual Representation: WenJi Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou

21. Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation: Yang Gao, Christian Meyer, Mohsen Mesgar, Iryna Gurevych

22. Sharing Experience in Multitask Reinforcement Learning: Tung-Long Vuong, Do-Van Nguyen, Tai-Long Nguyen, Cong-Minh Bui, Hai-Dang Kieu, Viet-Cuong Ta, Quoc-Long Tran, Thanh-Ha Le

23. SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets: Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, Craig Boutilier

24. Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning: Wenjie Shi, Shiji Song, Cheng Wu

25. Solving Continual Combinatorial Selection via Deep Reinforcement Learning: HyungSeok Song, Hyeryung Jang, Hai H. Tran, Se-eun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, Yung Yi

26. Successor Options: An Option Discovery Framework for Reinforcement Learning: Rahul Ramesh, Manan Tomar, Balaraman Ravindran

27. Transfer of Temporal Logic Formulas in Reinforcement Learning: Zhe Xu, Ufuk Topcu

28. Using Natural Language for Reward Shaping in Reinforcement Learning: Prasoon Goyal, Scott Niekum, Raymond Mooney

29. Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns: Yong Liu, Yujing Hu, Yang Gao, Yingfeng Chen, Changjie Fan

30. Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving: Akifumi Wachi

31. LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning: Alberto Camacho, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila McIlraith

32. A Survey of Reinforcement Learning Informed by Natural Language: Jelena Luketina↵, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstett, Shimon Whiteson, Tim Rocktäschel

33. Leveraging Human Guidance for Deep Reinforcement Learning Tasks: Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, Peter Stone

34. CRSRL: Customer Routing System using Reinforcement Learning: Chong Long, Zining Liu, Xiaolu Lu, Zehong Hu, Yafang Wang

35. Deep Reinforcement Learning for Ride-sharing Dispatching and Repositioning: Zhiwei (Tony) Qin, Xiaocheng Tang, Yan Jiao, Fan Zhang, Chenxi Wang

36. Learning Deep Decentralized Policy Network by Collective Rewards for Real-Time Combat Game: Peixi Peng, Junliang Xing, Lili Cao, Lisen Mu, Chang Huang

37. Monte Carlo Tree Search for Policy Optimization: Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang, Mykel J. Kochenderfer

38. On Principled Entropy Exploration in Policy Optimization: Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller

39. Recurrent Existence Determination Through Policy Optimization: Baoxiang Wang

40. Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies: Muhammad Masood, Finale Doshi-Velez

41. A probabilistic logic for resource-bounded multi-agent systems: Hoang Nga Nguyen, Abdur Rakib

42. A Value-based Trust Assessment Model for Multi-agent Systems: Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Hoa Khanh Dam

43. Branch-and-Cut-and-Price for Multi-Agent Pathfinding: Edward Lam, Pierre Le Bodic, Daniel Harabor, Peter J. Stuckey

44. Decidability of Model Checking Multi-Agent Systems with Regular Expressions against Epistemic HS Specifications: Jakub Michaliszyn, Piotr Witkowski

45. Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search: Jiaoyang Li, Eli Boyarski, Ariel Felner, Hang Ma, Sven Koenig

46. Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty: Minglong Li, Wenjing Yang, Zhongxuan Cai, Shaowu Yang, Ji Wang

47. Multi-agent Attentional Activity Recognition: Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, Zhiwen Yu

48. Multi-Agent Pathfinding with Continuous Time: Anton Andreychuk, Konstantin Yakovlev, Dor Atzmon, Roni Stern

49. Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding: Keisuke Okumura, Manao Machida, Xavier Défago, Yasumasa Tamura

50. The Interplay of Emotions and Norms in Multiagent Systems: Anup K. Kalia, Nirav Ajmeri, Kevin S. Chan, Jin-Hee Cho, Sibel Adali, Munindar Singh

51. Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories: Pavel Surynek

52. Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract): Bernhard Nebel, Thomas Bolander, Thorsten Engesser, Robert Mattmüller

53. Embodied Conversational AI Agents in a Multi-modal Multi-agent Competitive Dialogue: Rahul Divekar, Xiangyang Mou, Lisha Chen, Maíra Gatti de Bayser, Melina Alberio Guerra, Hui Su

54. Multi-Agent Path Finding on Ozobots: Roman Barták, Ivan Krasičenko, Jiří Švancara

55. Multi-Agent Visualization for Explaining Federated Learning: Xiguang Wei, Quan Li, Yang Liu, Han Yu, Tianjian Chen, Qiang Yang

56. Automated Machine Learning with Monte-Carlo Tree Search: Herilalaina Rakotoarison, Marc Schoenauer, Michele Sebag

57. Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning: Alberto Castellini, Georgios Chalkiadakis, Alessandro Farinelli

58. Multiple Policy Value Monte Carlo Tree Search: Li-Cheng Lan, Wei Li, Ting han Wei, I-Chen Wu

59. Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search: Thomas Gabor, Jan Peter, Thomy Phan, Christian Meyer, Claudia Linnhoff-Popien

60. A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification: Shaohuai Shi, Kaiyong Zhao, Qiang Wang, Zhenheng Tang, Xiaowen Chu

61. AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems: Yanchen Deng, Ziyu Chen, Dingding Chen, Wenxin Zhang, Xingqiong Jiang

62. Distributed Collaborative Feature Selection Based on Intermediate Representation: Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai

63. FABA: An Algorithm for Fast Aggregation against Byzantine Attacks in Distributed Neural Networks: Qi Xia, Zeyi Tao, Zijiang Hao, Qun Li

64. Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent: Shuheng Shen, Linli Xu, Jingchang Liu, Xianfeng Liang, Yifei Cheng

65. Fully Distributed Bayesian Optimization with Stochastic Policies: Javier Garcia-Barcos, Ruben Martinez-Cantin

Github链接

https://github.com/NeuronDance/DeepRL/tree/master/DRL-ConferencePaper/IJCAI

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