2019-Reinforcement-Learning-Conferences-Papers

The proceedings of top conference in 2019 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

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Todo

• Related repository
• AAAI'2019
• AAMAS'2019
• ICLR'2019
• ICML'2019
• ICRA'2019
• IJCAI'2019
• NeurIPS'2019

Contributing

Markdown format：

- **Paper Name**.
  [[pdf](link)]
  [[code](link)]
  - Author 1, Author 2, and Author 3. *conference, year*.

Please help to contribute this list by contacting me or add pull request.

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Table of Contents

• AAAI Conference on Artificial Intelligence (AAAI'2019)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2019)
• International Conference on Learning Representations (ICLR'2019)
• International Conference on Machine Learning (ICML'2019)
• International Conference on Robotics and Automation (ICRA'2019)
• International Joint Conference on Artificial Intelligence (IJCAI'2019)
• Annual Conference on Neural Information Processing Systems (NeurIPS'2019)

AAAI Conference on Artificial Intelligence

• Surveys without Questions: A Reinforcement Learning Approach. [pdf]
  • Atanu R. Sinha, Deepali Jain, Nikhil Sheoran, Sopan Khosla, Reshmi Sasidharan. AAAI 2019.
• Hierarchical Reinforcement Learning for Course Recommendation in MOOCs. [pdf]
  • Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, Jimeng Sun. AAAI 2019.
• A Model-Free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy Education. [pdf]
  • Hae Won Park, Ishaan Grover, Samuel Spaulding, Louis Gomez, Cynthia Breazeal. AAAI 2019.
• VidyutVanika: A Reinforcement Learning Based Broker Agent for a Power Trading Competition. [pdf]
  • Susobhan Ghosh, Easwar Subramanian, Sanjay P. Bhat, Sujit Gujar, Praveen Paruchuri. AAAI 2019.
• Deep Reinforcement Learning for Syntactic Error Repair in Student Programs. [pdf]
  • Rahul Gupta, Aditya Kanade, Shirish K. Shevade. AAAI 2019.
• A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems. [pdf]
  • Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang. AAAI 2019.
• Deep Reinforcement Learning for Green Security Games with Real-Time Information. [pdf]
  • Yufei Wang, Zheyuan Ryan Shi, Lantao Yu, Yi Wu, Rohit Singh, Lucas Joppa, Fei Fang. AAAI 2019.
• Improving Optimization Bounds Using Machine Learning: Decision Diagrams Meet Deep Reinforcement Learning. [pdf]
  • Quentin Cappart, Emmanuel Goutierre, David Bergman, Louis-Martin Rousseau. AAAI 2019.
• Generation of Policy-Level Explanations for Reinforcement Learning. [pdf]
  • Nicholay Topin, Manuela Veloso. AAAI 2019.
• Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning. [pdf]
  • Ziyu Yao, Xiujun Li, Jianfeng Gao, Brian M. Sadler, Huan Sun. AAAI 2019.
• SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning. [pdf]
  • Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson. AAAI 2019.
• End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. [pdf]
  • Richard Cheng, Gábor Orosz, Richard M. Murray, Joel W. Burdick. AAAI 2019.
• How to Combine Tree-Search Methods in Reinforcement Learning. [pdf]
  • Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor. AAAI 2019.
• Combined Reinforcement Learning via Abstract Representations. [pdf]
  • Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau. AAAI 2019.
• Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing. [pdf]
  • Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki. AAAI 2019.
• Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift. [pdf]
  • Carles Gelada, Marc G. Bellemare. AAAI 2019.
• Hybrid Reinforcement Learning with Expert State Sequences. [pdf]
  • Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell. AAAI 2019.
• Multi-Task Deep Reinforcement Learning with PopArt. [pdf]
  • Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt. AAAI 2019.
• Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning. [pdf]
  • Wenzhen Huang, Junge Zhang, Kaiqi Huang. AAAI 2019.
• Classification with Costly Features Using Deep Reinforcement Learning. [pdf]
  • Jaromír Janisch, Tomás Pevný, Viliam Lisý. AAAI 2019.
• Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient. [pdf]
  • Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, Stuart Russell. AAAI 2019.
• The Utility of Sparse Representations for Control in Reinforcement Learning. [pdf]
  • Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha White. AAAI 2019.
• A Comparative Analysis of Expected and Distributional Reinforcement Learning. [pdf]
  • Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro. AAAI 2019.
• State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning. [pdf]
  • Shuai Ma, Jia Yuan Yu. AAAI 2019.
• Determinantal Reinforcement Learning. [pdf]
  • Takayuki Osogami, Rudy Raymond. AAAI 2019.
• On Reinforcement Learning for Full-Length Game of StarCraft. [pdf]
  • Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu. AAAI 2019.
• Virtual-Taobao: Virtualizing Real-World Online Retail Environment for Reinforcement Learning. [pdf]
  • Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, Anxiang Zeng. AAAI 2019.
• Composable Modular Reinforcement Learning. [pdf]
  • Christopher L. Simpkins, Charles L. Isbell Jr.. AAAI 2019.
• Diversity-Driven Extensible Hierarchical Reinforcement Learning. [pdf]
  • Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Mai Xu. AAAI 2019.
• QUOTA: The Quantile Option Architecture for Reinforcement Learning. [pdf]
  • Shangtong Zhang, Hengshuai Yao. AAAI 2019.
• Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning. [pdf]
  • Woojun Kim, Myungsik Cho, Youngchul Sung. AAAI 2019.
• Learning to Teach in Cooperative Multiagent Reinforcement Learning. [pdf]
  • Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How. AAAI 2019.
• Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning. [pdf]
  • Ziming Li, Julia Kiseleva, Maarten de Rijke. AAAI 2019.
• A Hierarchical Framework for Relation Extraction with Reinforcement Learning. [pdf]
  • Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, Minlie Huang. AAAI 2019.
• A Deep Reinforcement Learning Based Multi-Step Coarse to Fine Question Answering (MSCQA) System. [pdf]
  • Yu Wang, Hongxia Jin. AAAI 2019.
• Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. [pdf]
  • Daniel S. Brown, Scott Niekum. AAAI 2019.
• Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach. [pdf]
  • Silviu Pitis. AAAI 2019.
• Attention-Aware Sampling via Deep Reinforcement Learning for Action Recognition. [pdf]
  • Wenkai Dong, Zhaoxiang Zhang, Tieniu Tan. AAAI 2019.
• Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos. [pdf]
  • Dongliang He, Xiang Zhao, Jizhou Huang, Fu Li, Xiao Liu, Shilei Wen. AAAI 2019.
• Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation. [pdf]
  • Qiuyuan Huang, Zhe Gan, Asli Celikyilmaz, Dapeng Oliver Wu, Jianfeng Wang, Xiaodong He. AAAI 2019.
• A Theory of State Abstraction for Reinforcement Learning. [pdf]
  • David Abel. AAAI 2019.
• Reinforcement Learning for Improved Low Resource Dialogue Generation. [pdf]
  • Ana Valeria González-Garduño. AAAI 2019.
• Verifiable and Interpretable Reinforcement Learning through Program Synthesis. [pdf]
  • Abhinav Verma. AAAI 2019.
• Attention Guided Imitation Learning and Reinforcement Learning. [pdf]
  • Ruohan Zhang. AAAI 2019.
• Reinforcement Learning under Threats. [pdf]
  • Víctor Gallego, Roi Naveiro, David Ríos Insua. AAAI 2019.
• Dynamic Vehicle Traffic Control Using Deep Reinforcement Learning in Automated Material Handling System. [pdf]
  • Younkook Kang, Sungwon Lyu, Jeeyung Kim, Bongjoon Park, Sungzoon Cho. AAAI 2019.
• Deep Reinforcement Learning via Past-Success Directed Exploration. [pdf]
  • Xiaoming Liu, Zhixiong Xu, Lei Cao, Xiliang Chen, Kai Kang. AAAI 2019.
• Strategic Tasks for Explainable Reinforcement Learning. [pdf]
  • Rey Pocius, Lawrence Neal, Alan Fern. AAAI 2019.
• Learning Representations in Model-Free Hierarchical Reinforcement Learning. [pdf]
  • Jacob Rafati, David C. Noelle. AAAI 2019.
• Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search. [pdf]
  • Helge Spieker. AAAI 2019.
• MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning. [pdf]
  • Manan Tomar, Akhil Sathuluri, Balaraman Ravindran. AAAI 2019.
• Geometric Multi-Model Fitting by Deep Reinforcement Learning. [pdf]
  • Zongliang Zhang, Hongbin Zeng, Jonathan Li, Yiping Chen, Chenhui Yang, Cheng Wang. AAAI 2019.

International Conference on Autonomous Agents and Multiagent Systems

• Building Knowledge for AI Agents with Reinforcement Learning. [pdf]
  • Doina Precup. AAMAS 2019.
• Bayesian Reinforcement Learning in Factored POMDPs. [pdf]
  • Sammie Katt, Frans A. Oliehoek, Christopher Amato. AAMAS 2019.
• Learning Curriculum Policies for Reinforcement Learning. [pdf]
  • Sanmit Narvekar, Peter Stone. AAMAS 2019.
• Model Primitive Hierarchical Lifelong Reinforcement Learning. [pdf]
  • Bohan Wu, Jayesh K. Gupta, Mykel J. Kochenderfer. AAMAS 2019.
• Negative Update Intervals in Deep Multi-Agent Reinforcement Learning. [pdf]
  • Gregory Palmer, Rahul Savani, Karl Tuyls. AAMAS 2019.
• Self-Improving Generative Adversarial Reinforcement Learning. [pdf]
  • Yang Liu, Yifeng Zeng, Yingke Chen, Jing Tang, Yinghui Pan. AAMAS 2019.
• Reinforcement Learning in Stationary Mean-field Games. [pdf]
  • Jayakumar Subramanian, Aditya Mahajan. AAMAS 2019.
• RLBOA: A Modular Reinforcement Learning Framework for Autonomous Negotiating Agents. [pdf]
  • Jasper Bakker, Aron Hammond, Daan Bloembergen, Tim Baarslag. AAMAS 2019.
• Reinforcement Learning for Cooperative Overtaking. [pdf]
  • Chao Yu, Xin Wang, Jianye Hao, Zhanbo Feng. AAMAS 2019.
• Urban Driving with Multi-Objective Deep Reinforcement Learning. [pdf]
  • Changjian Li, Krzysztof Czarnecki. AAMAS 2019.
• How You Act Tells a Lot: Privacy-Leaking Attack on Deep Reinforcement Learning. [pdf]
  • Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang, Bo Li, Jinfeng Yi, Dawn Song. AAMAS 2019.
• Newtonian Action Advice: Integrating Human Verbal Instruction with Reinforcement Learning. [pdf]
  • Samantha Krening, Karen M. Feigh. AAMAS 2019.
• Using Reinforcement Learning to Optimize the Policies of an Intelligent Tutoring System for Interpersonal Skills Training. [pdf]
  • Kallirroi Georgila, Mark G. Core, Benjamin D. Nye, Shamya Karumbaiah, Daniel Auerbach, Maya Ram. AAMAS 2019.
• A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network. [pdf]
  • Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, Tie-Yan Liu. AAMAS 2019.
• TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning. [pdf]
  • Longxiang Shi, Shijian Li, Longbing Cao, Long Yang, Gang Pan. AAMAS 2019.
• Improved Cooperative Multi-agent Reinforcement Learning Algorithm Augmented by Mixing Demonstrations from Centralized Policy. [pdf]
  • Hyun-Rok Lee, Taesik Lee. AAMAS 2019.
• Malthusian Reinforcement Learning. [pdf]
  • Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel. AAMAS 2019.
• Observational Learning by Reinforcement Learning. [pdf]
  • Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin. AAMAS 2019.
• Online Inverse Reinforcement Learning Under Occlusion. [pdf]
  • Saurabh Arora, Prashant Doshi, Bikramjit Banerjee. AAMAS 2019.
• Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? [pdf]
  • Yujie Chen, Yu Qian, Yichen Yao, Zili Wu, Rongqi Li, Yinzhi Zhou, Haoyuan Hu, Yinghui Xu. AAMAS 2019.
• Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning. [pdf]
  • Giulio Bacchiani, Daniele Molinari, Marco Patander. AAMAS 2019.
• Towards Decentralized Reinforcement Learning Architectures for Social Dilemmas. [pdf]
  • Nicolas Anastassacos, Mirco Musolesi. AAMAS 2019.
• Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning. [pdf]
  • Souvik Barat, Harshad Khadilkar, Hardik Meisheri, Vinay Kulkarni, Vinita Baniwal, Prashant Kumar, Monika Gajrani. AAMAS 2019.
• Attention-based Deep Reinforcement Learning for Multi-view Environments. [pdf]
  • Elaheh Barati, Xuewen Chen, Zichun Zhong. AAMAS 2019.
• Training Cooperative Agents for Multi-Agent Reinforcement Learning. [pdf]
  • Sushrut Bhalla, Sriram Ganapathi Subramanian, Mark Crowley. AAMAS 2019.
• Domain Adaptation for Reinforcement Learning on the Atari. [pdf]
  • Thomas Carr, Maria Chli, George Vogiatzis. AAMAS 2019.
• The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning. [pdf]
  • Jacopo Castellini, Frans A. Oliehoek, Rahul Savani, Shimon Whiteson. AAMAS 2019.
• Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains. [pdf]
  • Jim Martin Catacora Ocana, Francesco Riccio, Roberto Capobianco, Daniele Nardi. AAMAS 2019.
• Collaborative Reinforcement Learning Model for Sustainability of Cooperation in Sequential Social Dilemmas. [pdf]
  • Ritwik Chaudhuri, Kushal Mukherjee, Ramasuri Narayanam, Rohith Dwarakanath Vallam, Ayush Kumar, Antriksh Mathur, Shweta Garg, Sudhanshu Singh, Gyana R. Parija. AAMAS 2019.
• Reinforcement Learning with Derivative-Free Exploration. [pdf]
  • Xiong-Hui Chen, Yang Yu. AAMAS 2019.
• MARL-PPS: Multi-agent Reinforcement Learning with Periodic Parameter Sharing. [pdf]
  • Safa Cicek, Alireza Nakhaei, Stefano Soatto, Kikuo Fujimura. AAMAS 2019.
• Landmark Based Reward Shaping in Reinforcement Learning with Hidden States. [pdf]
  • Alper Demir, Erkin Çilden, Faruk Polat. AAMAS 2019.
• Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning. [pdf]
  • Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda, Prabuchandran K. J., Shalabh Bhatnagar. AAMAS 2019.
• Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. [pdf]
  • Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts. AAMAS 2019.
• A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. [pdf]
  • Francisco M. Garcia, Philip S. Thomas. AAMAS 2019.
• Multi-Agent Hierarchical Reinforcement Learning with Dynamic Termination. [pdf]
  • Dongge Han, Wendelin Boehmer, Michael J. Wooldridge, Alex Rogers. AAMAS 2019.
• Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning. [pdf]
  • Jacob Menashe, Peter Stone. AAMAS 2019.
• Object Exchangability in Reinforcement Learning. [pdf]
  • John Mern, Dorsa Sadigh, Mykel J. Kochenderfer. AAMAS 2019.
• Coordination Structures Generated by Deep Reinforcement Learning in Distributed Task Executions. [pdf]
  • Yuki Miyashita, Toshiharu Sugawara. AAMAS 2019.
• Effects of Task Similarity on Policy Transfer with Selective Exploration in Reinforcement Learning. [pdf]
  • Akshay Narayan, Tze-Yun Leong. AAMAS 2019.
• Risk Averse Reinforcement Learning for Mixed Multi-agent Environments. [pdf]
  • Sai Koti Reddy Danda, Amrita Saha, Srikanth G. Tamilselvam, Priyanka Agrawal, Pankaj Dayama. AAMAS 2019.
• A Regulation Enforcement Solution for Multi-agent Reinforcement Learning. [pdf]
  • Fan-Yun Sun, Yen-Yu Chang, Yueh-Hua Wu, Shou-De Lin. AAMAS 2019.
• MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning. [pdf]
  • Manan Tomar, Akhil Sathuluri, Balaraman Ravindran. AAMAS 2019.
• A Reinforcement Learning Framework for Container Selection and Ship Load Sequencing in Ports. [pdf]
  • Richa Verma, Sarmimala Saikia, Harshad Khadilkar, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan. AAMAS 2019.
• Multiagent Adversarial Inverse Reinforcement Learning. [pdf]
  • Ermo Wei, Drew Wicke, Sean Luke. AAMAS 2019.
• Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Reinforcement Learning Framework. [pdf]
  • Yaodong Yang, Jianye Hao, Yan Zheng, Xiaotian Hao, Bofeng Fu. AAMAS 2019.
• Coordinated Multiagent Reinforcement Learning for Teams of Mobile Sensing Robots. [pdf]
  • Chao Yu, Xin Wang, Zhanbo Feng. AAMAS 2019.
• Automatic Feature Engineering by Deep Reinforcement Learning. [pdf]
  • Jianyu Zhang, Jianye Hao, Françoise Fogelman-Soulié, Zan Wang. AAMAS 2019.
• Improving Deep Reinforcement Learning via Transfer. [pdf]
  • Yunshu Du. AAMAS 2019.
• Integrating Agent Advice and Previous Task Solutions in Multiagent Reinforcement Learning. [pdf]
  • Felipe Leno da Silva. AAMAS 2019.

International Conference on Learning Representations

• Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees. [pdf]
  • Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma. ICLR 2019.
• M^3RL: Mind-aware Multi-agent Management Reinforcement Learning. [pdf]
  • Tianmin Shu, Yuandong Tian. ICLR 2019.
• Information-Directed Exploration for Deep Reinforcement Learning. [pdf]
  • Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause. ICLR 2019.
• Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. [pdf]
  • Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine. ICLR 2019.
• Adversarial Imitation via Variational Inverse Reinforcement Learning. [pdf]
  • Ahmed Hussain Qureshi, Byron Boots, Michael C. Yip. ICLR 2019.
• Variance Reduction for Reinforcement Learning in Input-Driven Environments. [pdf]
  • Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh. ICLR 2019.
• Recall Traces: Backtracking Models for Efficient Reinforcement Learning. [pdf]
  • Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio. ICLR 2019.
• Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. [pdf]
  • Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama. ICLR 2019.
• Contingency-Aware Exploration in Reinforcement Learning. [pdf]
  • Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee. ICLR 2019.
• Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning. [pdf]
  • Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn. ICLR 2019.
• Supervised Policy Update for Deep Reinforcement Learning. [pdf]
  • Quan Ho Vuong, Yiming Zhang, Keith W. Ross. ICLR 2019.
• Learning to Schedule Communication in Multi-agent Reinforcement Learning. [pdf]
  • Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, Yung Yi. ICLR 2019.
• Modeling the Long Term Future in Model-Based Reinforcement Learning. [pdf]
  • Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra. ICLR 2019.
• Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards. [pdf]
  • Daniel McDuff, Ashish Kapoor. ICLR 2019.
• From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following. [pdf]
  • Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama. ICLR 2019.
• Recurrent Experience Replay in Distributed Reinforcement Learning. [pdf]
  • Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney. ICLR 2019.
• Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning. [pdf]
  • Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan. ICLR 2019.

International Conference on Machine Learning

• Dynamic Weights in Multi-Objective Deep Reinforcement Learning. [pdf]
  • Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher. ICML 2019.
• TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning. [pdf]
  • Tameem Adel, Adrian Weller. ICML 2019.
• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. [pdf]
  • Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum. ICML 2019.
• Learning Action Representations for Reinforcement Learning. [pdf]
  • Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas. ICML 2019.
• Information-Theoretic Considerations in Batch Reinforcement Learning. [pdf]
  • Jinglin Chen, Nan Jiang. ICML 2019.
• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System. [pdf]
  • Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song. ICML 2019.
• Control Regularization for Reduced Variance Reinforcement Learning. [pdf]
  • Richard Cheng, Abhinav Verma, Gábor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick. ICML 2019.
• Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. [pdf]
  • Casey Chu, Jose H. Blanchet, Peter W. Glynn. ICML 2019.
• Quantifying Generalization in Reinforcement Learning. [pdf]
  • Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman. ICML 2019.
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning. [pdf]
  • Cédric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouani. ICML 2019.
• The Value Function Polytope in Reinforcement Learning. [pdf]
  • Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans. ICML 2019.
• Policy Certificates: Towards Accountable Reinforcement Learning. [pdf]
  • Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill. ICML 2019.
• Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning. [pdf]
  • Thinh T. Doan, Siva Theja Maguluri, Justin Romberg. ICML 2019.
• Trajectory-Based Off-Policy Deep Reinforcement Learning. [pdf]
  • Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel. ICML 2019.
• Task-Agnostic Dynamics Priors for Deep Reinforcement Learning. [pdf]
  • Yilun Du, Karthik Narasimhan. ICML 2019.
• Dead-ends and Secure Exploration in Reinforcement Learning. [pdf]
  • Mehdi Fatemi, Shikhar Sharma, Harm van Seijen, Samira Ebrahimi Kahou. ICML 2019.
• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. [pdf]
  • Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling. ICML 2019.
• Off-Policy Deep Reinforcement Learning without Exploration. [pdf]
  • Scott Fujimoto, David Meger, Doina Precup. ICML 2019.
• Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation. [pdf]
  • Shani Gamrian, Yoav Goldberg. ICML 2019.
• Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. [pdf]
  • Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang. ICML 2019.
• Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning. [pdf]
  • Seungyul Han, Youngchul Sung. ICML 2019.
• Actor-Attention-Critic for Multi-Agent Reinforcement Learning. [pdf]
  • Shariq Iqbal, Fei Sha. ICML 2019.
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. [pdf]
  • Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas. ICML 2019.
• A Deep Reinforcement Learning Perspective on Internet Congestion Control. [pdf]
  • Nathan Jay, Noga H. Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar. ICML 2019.
• Neural Logic Reinforcement Learning. [pdf]
  • Zhengyao Jiang, Shan Luo. ICML 2019.
• Policy Consolidation for Continual Reinforcement Learning. [pdf]
  • Christos Kaplanis, Murray Shanahan, Claudia Clopath. ICML 2019.
• Collaborative Evolutionary Reinforcement Learning. [pdf]
  • Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer. ICML 2019.
• Kernel-Based Reinforcement Learning in Robust Markov Decision Processes. [pdf]
  • Shiau Hong Lim, Arnaud Autef. ICML 2019.
• Calibrated Model-Based Deep Reinforcement Learning. [pdf]
  • Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon. ICML 2019.
• Distributional Reinforcement Learning for Efficient Exploration. [pdf]
  • Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu. ICML 2019.
• Reinforcement Learning in Configurable Continuous Environments. [pdf]
  • Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli. ICML 2019.
• Fingerprint Policy Optimisation for Robust Reinforcement Learning. [pdf]
  • Supratik Paul, Michael A. Osborne, Shimon Whiteson. ICML 2019.
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. [pdf]
  • Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen. ICML 2019.
• Statistics and Samples in Distributional Reinforcement Learning. [pdf]
  • Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney. ICML 2019.
• Exploration Conscious Reinforcement Learning Revisited. [pdf]
  • Lior Shani, Yonathan Efroni, Shie Mannor. ICML 2019.
• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Hostallero, Yung Yi. ICML 2019.
• Action Robust Reinforcement Learning and Applications in Continuous Control. [pdf]
  • Chen Tessler, Yonathan Efroni, Shie Mannor. ICML 2019.
• Composing Value Functions in Reinforcement Learning. [pdf]
  • Benjamin van Niekerk, Steven D. James, Adam Christopher Earle, Benjamin Rosman. ICML 2019.
• On the Generalization Gap in Reparameterizable Reinforcement Learning. [pdf]
  • Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher. ICML 2019.
• Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. [pdf]
  • Kelvin Xu, Ellis Ratner, Anca D. Dragan, Sergey Levine, Chelsea Finn. ICML 2019.
• Multi-Agent Adversarial Inverse Reinforcement Learning. [pdf]
  • Lantao Yu, Jiaming Song, Stefano Ermon. ICML 2019.
• Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. [pdf]
  • Andrea Zanette, Emma Brunskill. ICML 2019.
• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. [pdf]
  • Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine. ICML 2019.
• Maximum Entropy-Regularized Multi-Goal Reinforcement Learning. [pdf]
  • Rui Zhao, Xudong Sun, Volker Tresp. ICML 2019.

International Conference on Robotics and Automation

• BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning. [pdf]
  • Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone. ICRA 2019.
• VPE: Variational Policy Embedding for Transfer Reinforcement Learning. [pdf]
  • Isac Arnekvist, Danica Kragic, Johannes A. Stork. ICRA 2019.
• Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped. [pdf]
  • Tianyu Li, Hartmut Geyer, Christopher G. Atkeson, Akshara Rai. ICRA 2019.
• Reinforcement Learning Meets Hybrid Zero Dynamics: A Case Study for RABBIT. [pdf]
  • Guillermo A. Castillo, Bowen Weng, Ayonga Hereid, Zheng Wang, Wei Zhang. ICRA 2019.
• A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning. [pdf]
  • Mel Vecerík, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jonathan Scholz. ICRA 2019.
• Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation. [pdf]
  • Weihao Yuan, Kaiyu Hang, Haoran Song, Danica Kragic, Michael Yu Wang, Johannes A. Stork. ICRA 2019.
• Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction. [pdf]
  • Sammy Joe Christen, Stefan Stevsic, Otmar Hilliges. ICRA 2019.
• Inverse Reinforcement Learning of Interaction Dynamics from Demonstrations. [pdf]
  • Mostafa Hussein, Momotaz Begum, Marek Petrik. ICRA 2019.
• Offline Policy Iteration Based Reinforcement Learning Controller for Online Robotic Knee Prosthesis Parameter Tuning. [pdf]
  • Minhan Li, Xiang Gao, Yue Wen, Jennie Si, He Helen Huang. ICRA 2019.
• Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly. [pdf]
  • Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino, Aviv Tamar, Pieter Abbeel. ICRA 2019.
• Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals. [pdf]
  • Anahita Mohseni-Kabir, David Isele, Kikuo Fujimura. ICRA 2019.
• Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning. [pdf]
  • Macheng Shen, Jonathan P. How. ICRA 2019.
• Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. [pdf]
  • Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar. ICRA 2019.
• OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras. [pdf]
  • G. Dias Pais, Tiago J. Dias, Jacinto C. Nascimento, Pedro Miraldo. ICRA 2019.
• Open Loop Position Control of Soft Continuum Arm Using Deep Reinforcement Learning. [pdf]
  • Sreeshankar Satheeshbabu, Naveen Kumar Uppalapati, Girish Chowdhary, Girish Krishnan. ICRA 2019.
• Deep Reinforcement Learning of Navigation in a Complex and Crowded Environment with a Limited Field of View. [pdf]
  • Jinyoung Choi, Kyungsik Park, Minsu Kim, Sangok Seok. ICRA 2019.
• Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight. [pdf]
  • Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine. ICRA 2019.
• Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning. [pdf]
  • Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi. ICRA 2019.
• Residual Reinforcement Learning for Robot Control. [pdf]
  • Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine. ICRA 2019.
• A Reinforcement Learning Approach for Control of a Nature-Inspired Aerial Vehicle. [pdf]
  • Danial Sufiyan Bin Shaiful, Luke Thura Soe Win, Shane Kyi Hla Win, Gim Song Soh, Shaohui Foong. ICRA 2019.
• Continuous Value Iteration (CVI) Reinforcement Learning and Imaginary Experience Replay (IER) For Learning Multi-Goal, Continuous Action and State Space Controllers. [pdf]
  • Andreas Gerken, Michael Spranger. ICRA 2019.
• Risk Averse Robust Adversarial Reinforcement Learning. [pdf]
  • Xinlei Pan, Daniel Seita, Yang Gao, John F. Canny. ICRA 2019.
• Early Failure Detection of Deep End-to-End Control Policy by Reinforcement Learning. [pdf]
  • Keuntaek Lee, Kamil Saigol, Evangelos A. Theodorou. ICRA 2019.
• Bridging Hamilton-Jacobi Safety Analysis and Reinforcement Learning. [pdf]
  • Jaime F. Fisac, Neil F. Lugovoy, Vicenç Rúbies Royo, Shromona Ghosh, Claire J. Tomlin. ICRA 2019.
• Safe Reinforcement Learning With Model Uncertainty Estimates. [pdf]
  • Björn Lütjens, Michael Everett, Jonathan P. How. ICRA 2019.
• Distributional Deep Reinforcement Learning with a Mixture of Gaussians. [pdf]
  • Yunho Choi, Kyungjae Lee, Songhwai Oh. ICRA 2019.
• Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning. [pdf]
  • Charles B. Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter. ICRA 2019.

International Joint Conference on Artificial Intelligence

• Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns. [pdf]
  • Yong Liu, Yujing Hu, Yang Gao, Yingfeng Chen, Changjie Fan. IJCAI 2019.
• Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning Framework. [pdf]
  • Yaodong Yang, Jianye Hao, Yan Zheng, Chao Yu. IJCAI 2019.
• Explaining Reinforcement Learning to Mere Mortals: An Empirical Study. [pdf]
  • Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett. IJCAI 2019.
• An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments. [pdf]
  • Elaheh Barati, Xuewen Chen. IJCAI 2019.
• A Restart-based Rank-1 Evolution Strategy for Reinforcement Learning. [pdf]
  • Zefeng Chen, Yuren Zhou, Xiaoyu He, Siyu Jiang. IJCAI 2019.
• Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space. [pdf]
  • Zhou Fan, Rui Su, Weinan Zhang, Yong Yu. IJCAI 2019.
• Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces. [pdf]
  • Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, Changjie Fan. IJCAI 2019.
• Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards. [pdf]
  • Zhao-Yang Fu, De-Chuan Zhan, Xin-Chun Li, Yi-Xing Lu. IJCAI 2019.
• Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation. [pdf]
  • Yang Gao, Christian M. Meyer, Mohsen Mesgar, Iryna Gurevych. IJCAI 2019.
• Using Natural Language for Reward Shaping in Reinforcement Learning. [pdf]
  • Prasoon Goyal, Scott Niekum, Raymond J. Mooney. IJCAI 2019.
• SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets. [pdf]
  • Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, Craig Boutilier. IJCAI 2019.
• Interactive Teaching Algorithms for Inverse Reinforcement Learning. [pdf]
  • Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, Adish Singla. IJCAI 2019.
• Autoregressive Policies for Continuous Control Deep Reinforcement Learning. [pdf]
  • Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra. IJCAI 2019.
• Meta Reinforcement Learning with Task Embedding and Shared Policy. [pdf]
  • Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang. IJCAI 2019.
• Incremental Learning of Planning Actions in Model-Based Reinforcement Learning. [pdf]
  • Jun Hao Alvin Ng, Ronald P. A. Petrick. IJCAI 2019.
• An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. [pdf]
  • Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman. IJCAI 2019.
• Successor Options: An Option Discovery Framework for Reinforcement Learning. [pdf]
  • Rahul Ramesh, Manan Tomar, Balaraman Ravindran. IJCAI 2019.
• Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning. [pdf]
  • Wenjie Shi, Shiji Song, Cheng Wu. IJCAI 2019.
• Solving Continual Combinatorial Selection via Deep Reinforcement Learning. [pdf]
  • HyungSeok Song, Hyeryung Jang, Hai H. Tran, Se-eun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, Yung Yi. IJCAI 2019.
• Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning. [pdf]
  • Shihong Song, Jiayi Weng, Hang Su, Dong Yan, Haosheng Zou, Jun Zhu. IJCAI 2019.
• Sharing Experience in Multitask Reinforcement Learning. [pdf]
  • Tung-Long Vuong, Do Van Nguyen, Tai-Long Nguyen, Cong-Minh Bui, Hai-Dang Kieu, Viet-Cuong Ta, Quoc-Long Tran, Thanh Ha Le. IJCAI 2019.
• Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations. [pdf]
  • Zhaodong Wang, Matthew E. Taylor. IJCAI 2019.
• Transfer of Temporal Logic Formulas in Reinforcement Learning. [pdf]
  • Zhe Xu, Ufuk Topcu. IJCAI 2019.
• Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control. [pdf]
  • Kenny Young, Baoxiang Wang, Matthew E. Taylor. IJCAI 2019.
• Reinforcement Learning Experience Reuse with Policy Residual Representation. [pdf]
  • Wen-Ji Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou. IJCAI 2019.
• Playing Card-Based RTS Games with Deep Reinforcement Learning. [pdf]
  • Tianyu Liu, Zijie Zheng, Hongchang Li, Kaigui Bian, Lingyang Song. IJCAI 2019.
• Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks. [pdf]
  • Wei Qiu, Haipeng Chen, Bo An. IJCAI 2019.
• A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer. [pdf]
  • Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Xu Sun, Zhifang Sui. IJCAI 2019.
• Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning. [pdf]
  • Zhenshan Bing, Christian Lemke, Zhuangyi Jiang, Kai Huang, Alois C. Knoll. IJCAI 2019.
• Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving. [pdf]
  • Akifumi Wachi. IJCAI 2019.
• LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning. [pdf]
  • Alberto Camacho, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Anthony Valenzano, Sheila A. McIlraith. IJCAI 2019.
• A Survey of Reinforcement Learning Informed by Natural Language. [pdf]
  • Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel. IJCAI 2019.
• Leveraging Human Guidance for Deep Reinforcement Learning Tasks. [pdf]
  • Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, Peter Stone. IJCAI 2019.
• Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration. [pdf]
  • Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R. Varshney, Murray Campbell, Moninder Singh, Francesca Rossi. IJCAI 2019.
• Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?. [pdf]
  • Céline Hocquette. IJCAI 2019.
• Split Q Learning: Reinforcement Learning with Two-Stream Rewards. [pdf]
  • Baihan Lin, Djallel Bouneffouf, Guillermo A. Cecchi. IJCAI 2019.
• Safe and Sample-Efficient Reinforcement Learning Algorithms for Factored Environments. [pdf]
  • Thiago D. Simão. IJCAI 2019.
• CRSRL: Customer Routing System Using Reinforcement Learning. [pdf]
  • Chong Long, Zining Liu, Xiaolu Lu, Zehong Hu, Yafang Wang. IJCAI 2019.
• Deep Reinforcement Learning for Ride-sharing Dispatching and Repositioning. [pdf]
  • Zhiwei (Tony) Qin, Xiaocheng Tang, Yan Jiao, Fan Zhang, Chenxi Wang, Qun (Tracy) Li. IJCAI 2019.

Annual Conference on Neural Information Processing Systems

• Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling. [pdf]
  • Andrey Kolobov, Yuval Peres, Cheng Lu, Eric Horvitz. NeurIPS 2019.
• Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives. [pdf]
  • Wang Chi Cheung. NeurIPS 2019.
• Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. [pdf]
  • Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong. NeurIPS 2019.
• Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards. [pdf]
  • Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang. NeurIPS 2019.
• Multi-View Reinforcement Learning. [pdf]
  • Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar. NeurIPS 2019.
• Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update. [pdf]
  • Su Young Lee, Sung-Ik Choi, Sae-Young Chung. NeurIPS 2019.
• Information-Theoretic Confidence Bounds for Reinforcement Learning. [pdf]
  • Xiuyuan Lu, Benjamin Van Roy. NeurIPS 2019.
• Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function. [pdf]
  • Zihan Zhang, Xiangyang Ji. NeurIPS 2019.
• Real-Time Reinforcement Learning. [pdf]
  • Simon Ramstedt, Chris Pal. NeurIPS 2019.
• Convergent Policy Optimization for Safe Reinforcement Learning. [pdf]
  • Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang. NeurIPS 2019.
• Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control. [pdf]
  • Sai Qian Zhang, Qi Zhang, Jieyu Lin. NeurIPS 2019.
• Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. [pdf]
  • Nathan Kallus, Masatoshi Uehara. NeurIPS 2019.
• Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. [pdf]
  • Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla. NeurIPS 2019.
• Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters. [pdf]
  • Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli. NeurIPS 2019.
• A Geometric Perspective on Optimal Representations for Reinforcement Learning. [pdf]
  • Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle. NeurIPS 2019.
• LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning. [pdf]
  • Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, Dacheng Tao. NeurIPS 2019.
• Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning. [pdf]
  • Harsh Gupta, R. Srikant, Lei Ying. NeurIPS 2019.
• Adaptive Auxiliary Task Weighting for Reinforcement Learning. [pdf]
  • Xingyu Lin, Harjatin Singh Baweja, George Kantor, David Held. NeurIPS 2019.
• A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. [pdf]
  • Francisco M. Garcia, Philip S. Thomas. NeurIPS 2019.
• A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. [pdf]
  • Wenhao Yang, Xiang Li, Zhihua Zhang. NeurIPS 2019.
• Fully Parameterized Quantile Function for Distributional Reinforcement Learning. [pdf]
  • Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu. NeurIPS 2019.
• Distributional Reward Decomposition for Reinforcement Learning. [pdf]
  • Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang. NeurIPS 2019.
• On the Correctness and Sample Complexity of Inverse Reinforcement Learning. [pdf]
  • Abi Komanduru, Jean Honorio. NeurIPS 2019.
• VIREL: A Variational Inference Framework for Reinforcement Learning. [pdf]
  • Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson. NeurIPS 2019.
• Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning. [pdf]
  • Erwan Lecarpentier, Emmanuel Rachelson. NeurIPS 2019.
• Explicit Planning for Efficient Exploration in Reinforcement Learning. [pdf]
  • Liangpeng Zhang, Ke Tang, Xin Yao. NeurIPS 2019.
• Constrained Reinforcement Learning Has Zero Duality Gap. [pdf]
  • Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro. NeurIPS 2019.
• SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. [pdf]
  • Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel. NeurIPS 2019.
• A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric. NeurIPS 2019.
• Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. [pdf]
  • Gregory Farquhar, Shimon Whiteson, Jakob N. Foerster. NeurIPS 2019.
• Budgeted Reinforcement Learning in Continuous State Space. [pdf]
  • Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin. NeurIPS 2019.
• Language as an Abstraction for Hierarchical Deep Reinforcement Learning. [pdf]
  • Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn. NeurIPS 2019.
• Non-Cooperative Inverse Reinforcement Learning. [pdf]
  • Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar. NeurIPS 2019.
• Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. [pdf]
  • Tengyang Xie, Yifei Ma, Yu-Xiang Wang. NeurIPS 2019.
• Multi-Agent Common Knowledge Reinforcement Learning. [pdf]
  • Christian Schröder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson. NeurIPS 2019.
• Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning. [pdf]
  • Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang. NeurIPS 2019.
• Unsupervised Curricula for Visual Meta-Reinforcement Learning. [pdf]
  • Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn. NeurIPS 2019.
• A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation. [pdf]
  • Xueying Bai, Jian Guan, Hongning Wang. NeurIPS 2019.
• Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. [pdf]
  • Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon. NeurIPS 2019.
• Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. [pdf]
  • Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor. NeurIPS 2019.
• Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. [pdf]
  • Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende. NeurIPS 2019.
• Regret Bounds for Learning State Representations in Reinforcement Learning. [pdf]
  • Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard. NeurIPS 2019.
• A Composable Specification Language for Reinforcement Learning Tasks. [pdf]
  • Kishor Jothimurugan, Rajeev Alur, Osbert Bastani. NeurIPS 2019.
• The Option Keyboard: Combining Skills in Reinforcement Learning. [pdf]
  • André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup. NeurIPS 2019.
• Biases for Emergent Communication in Multi-agent Reinforcement Learning. [pdf]
  • Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel. NeurIPS 2019.
• Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. [pdf]
  • Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat. NeurIPS 2019.
• Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. [pdf]
  • Junzhe Zhang, Elias Bareinboim. NeurIPS 2019.
• Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. [pdf]
  • Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann. NeurIPS 2019.
• Reinforcement Learning with Convex Constraints. [pdf]
  • Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire. NeurIPS 2019.
• Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning. [pdf]
  • Harm van Seijen, Mehdi Fatemi, Arash Tavakoli. NeurIPS 2019.
• Correlation Priors for Reinforcement Learning. [pdf]
  • Bastian Alt, Adrian Sosic, Heinz Koeppl. NeurIPS 2019.
• Policy Poisoning in Batch Reinforcement Learning and Control. [pdf]
  • Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu. NeurIPS 2019.
• A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation. [pdf]
  • Runzhe Yang, Xingyuan Sun, Karthik Narasimhan. NeurIPS 2019.
• Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning. [pdf]
  • Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen. NeurIPS 2019.
• Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. [pdf]
  • Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine. NeurIPS 2019.
• Learning Reward Machines for Partially Observable Reinforcement Learning. [pdf]
  • Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Anthony Valenzano, Margarita P. Castro, Sheila A. McIlraith. NeurIPS 2019.
• A Family of Robust Stochastic Operators for Reinforcement Learning. [pdf]
  • Yingdong Lu, Mark S. Squillante, Chai Wah Wu. NeurIPS 2019.
• Imitation-Projected Programmatic Reinforcement Learning. [pdf]
  • Abhinav Verma, Hoang Minh Le, Yisong Yue, Swarat Chaudhuri. NeurIPS 2019.