The proceedings of top conference in 2020 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

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Awesome-Reinforcement-Learning-Papers
Tutorial4RL
2023-Reinforcement-Learning-Conferences-Papers
2022-Reinforcement-Learning-Conferences-Papers
2021-Reinforcement-Learning-Conferences-Papers
2020-Reinforcement-Learning-Conferences-Papers
2019-Reinforcement-Learning-Conferences-Papers
2018-Reinforcement-Learning-Conferences-Papers
2017-Reinforcement-Learning-Conferences-Papers

• Related repository
• AAAI'2020
• AAMAS'2020
• ICLR'2020
• ICML'2020
• ICRA'2020
• IJCAI'2020
• NeurIPS'2020

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.

For any questions, feel free to contact me 📮.

• AAAI Conference on Artificial Intelligence (AAAI'2020)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2020)
• International Conference on Learning Representations (ICLR'2020)
• International Conference on Machine Learning (ICML'2020)
• International Conference on Robotics and Automation (ICRA'2020)
• International Joint Conference on Artificial Intelligence (IJCAI'2020)
• Annual Conference on Neural Information Processing Systems (NeurIPS'2020)

• The Unreasonable Effectiveness of Inverse Reinforcement Learning in Advancing Cancer Research. [pdf]
  • John Kalantari, Heidi Nelson, Nicholas Chia. AAAI 2020.
• Weak Supervision for Fake News Detection via Reinforcement Learning. [pdf]
  • Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, Jing Gao. AAAI 2020.
• RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning. [pdf]
  • Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang. AAAI 2020.
• MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning. [pdf]
  • Wentian Li, Xidong Feng, Haotian An, Xiang Yao Ng, Yu-Jin Zhang. AAAI 2020.
• Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning. [pdf]
  • Xiang Ni, Jing Li, Mo Yu, Wang Zhou, Kun-Lung Wu. AAAI 2020.
• Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning. [pdf]
  • Liang Tong, Aron Laszka, Chao Yan, Ning Zhang, Yevgeniy Vorobeychik. AAAI 2020.
• Actor Critic Deep Reinforcement Learning for Neural Malware Control. [pdf]
  • Yu Wang, Jack W. Stokes, Mady Marinescu. AAAI 2020.
• MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control. [pdf]
  • Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, Zhenhui Li. AAAI 2020.
• Transfer Reinforcement Learning Using Output-Gated Working Memory. [pdf]
  • Arthur Williams, Joshua Phillips. AAAI 2020.
• Deep Reinforcement Learning for General Game Playing. [pdf]
  • Adrian Goldwaser, Michael Thielscher. AAAI 2020.
• Model and Reinforcement Learning for Markov Games with Risk Preferences. [pdf]
  • Wenjie Huang, Pham Viet Hai, William Benjamin Haskell. AAAI 2020.
• Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach. [pdf]
  • Yang Liu, Qi Liu, Hongke Zhao, Zhen Pan, Chuanren Liu. AAAI 2020.
• Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning. [pdf]
  • Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe. AAAI 2020.
• Explainable Reinforcement Learning through a Causal Lens. [pdf]
  • Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere. AAAI 2020.
• BAR - A Reinforcement Learning Agent for Bounding-Box Automated Refinement. [pdf]
  • Morgane Ayle, Jimmy Tekli, Julia El Zini, Boulos El Asmar, Mariette Awad. AAAI 2020.
• Exploratory Combinatorial Optimization with Reinforcement Learning. [pdf]
  • Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky. AAAI 2020.
• Proximal Distilled Evolutionary Reinforcement Learning. [pdf]
  • Cristian Bodnar, Ben Day, Pietro Lió. AAAI 2020.
• Reinforcement Learning When All Actions Are Not Always Available. [pdf]
  • Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas. AAAI 2020.
• Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. [pdf]
  • Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang, Yuanhao Xiong, Kai Xu, Zhenhui Li. AAAI 2020.
• A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound. [pdf]
  • Gal Dalal, Balázs Szörényi, Gugan Thoppe. AAAI 2020.
• Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. [pdf]
  • Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves. AAAI 2020.
• Induction of Subgoal Automata for Reinforcement Learning. [pdf]
  • Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, Anders Jonsson. AAAI 2020.
• Reinforcement Learning with Non-Markovian Rewards. [pdf]
  • Maor Gaon, Ronen I. Brafman. AAAI 2020.
• Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. [pdf]
  • Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare. AAAI 2020.
• Google Research Football: A Novel Reinforcement Learning Environment. [pdf]
  • Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly. AAAI 2020.
• Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents. [pdf]
  • Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, Soumik Sarkar. AAAI 2020.
• Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance. [pdf]
  • Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang, Huaping Liu. AAAI 2020.
• Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations. [pdf]
  • Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz. AAAI 2020.
• Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks. [pdf]
  • Fabio Pardo, Vitaly Levdik, Petar Kormushev. AAAI 2020.
• Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents. [pdf]
  • Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor. AAAI 2020.
• Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning. [pdf]
  • Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu. AAAI 2020.
• Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning. [pdf]
  • Tian Tan, Zhihan Xiong, Vikranth R. Dwaracherla. AAAI 2020.
• Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks. [pdf]
  • Guojia Wan, Bo Du, Shirui Pan, Gholamreza Haffari. AAAI 2020.
• Reinforcement Learning with Perturbed Rewards. [pdf]
  • Jingkang Wang, Yang Liu, Bo Li. AAAI 2020.
• Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach. [pdf]
  • Jun Wang, Hefu Zhang, Qi Liu, Zhen Pan, Hanqing Tao. AAAI 2020.
• Mastering Complex Control in MOBA Games with Deep Reinforcement Learning. [pdf]
  • Deheng Ye, Zhao Liu, Mingfei Sun, Bei Shi, Peilin Zhao, Hao Wu, Hongsheng Yu, Shaojie Yang, Xipeng Wu, Qingwei Guo, Qiaobo Chen, Yinyuting Yin, Hao Zhang, Tengfei Shi, Liang Wang, Qiang Fu, Wei Yang, Lanxiao Huang. AAAI 2020.
• Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization. [pdf]
  • Qi Zhou, Houqiang Li, Jie Wang. AAAI 2020.
• Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning. [pdf]
  • Nicolas Anastassacos, Stephen Hailes, Mirco Musolesi. AAAI 2020.
• Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning. [pdf]
  • Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang, Zhen Xiao. AAAI 2020.
• SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Chao Wen, Xinghu Yao, Yuhui Wang, Xiaoyang Tan. AAAI 2020.
• LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games. [pdf]
  • Leonard Adolphs, Thomas Hofmann. AAAI 2020.
• Modelling Sentence Pairs via Reinforcement Learning: An Actor-Critic Approach to Learn the Irrelevant Words. [pdf]
  • Mahtab Ahmed, Robert E. Mercer. AAAI 2020.
• Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning. [pdf]
  • Liqun Chen, Ke Bai, Chenyang Tao, Yizhe Zhang, Guoyin Wang, Wenlin Wang, Ricardo Henao, Lawrence Carin. AAAI 2020.
• Hierarchical Reinforcement Learning for Open-Domain Dialog. [pdf]
  • Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Rosalind W. Picard. AAAI 2020.
• Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning. [pdf]
  • Liqiang Xiao, Lu Wang, Hao He, Yaohui Jin. AAAI 2020.
• Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model. [pdf]
  • Ya Xiao, Chengxiang Tan, Zhijie Fan, Qian Xu, Wenye Zhu. AAAI 2020.
• Be Relevant, Non-Redundant, and Timely: Deep Reinforcement Learning for Real-Time Event Summarization. [pdf]
  • Min Yang, Chengming Li, Fei Sun, Zhou Zhao, Ying Shen, Chenglin Wu. AAAI 2020.
• Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes. [pdf]
  • Tomás Brázdil, Krishnendu Chatterjee, Petr Novotný, Jiri Vahala. AAAI 2020.
• Modular Robot Design Synthesis with Deep Reinforcement Learning. [pdf]
  • Julian Whitman, Raunaq M. Bhirangi, Matthew J. Travers, Howie Choset. AAAI 2020.
• Deep Reinforcement Learning for Active Human Pose Estimation. [pdf]
  • Erik Gärtner, Aleksis Pirinen, Cristian Sminchisescu. AAAI 2020.
• Tree-Structured Policy Based Progressive Reinforcement Learning for Temporally Language Grounding in Video. [pdf]
  • Jie Wu, Guanbin Li, Si Liu, Liang Lin. AAAI 2020.
• Restraining Bolts for Reinforcement Learning Agents. [pdf]
  • Giuseppe De Giacomo, Luca Iocchi, Marco Favorito, Fabio Patrizi. AAAI 2020.
• Explainable Agency in Reinforcement Learning Agents. [pdf]
  • Prashan Madumal. AAAI 2020.
• A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence. [pdf]
  • Patrick Shepherd, Judy Goldsmith. AAAI 2020.
• Multi-Agent Pattern Formation with Deep Reinforcement Learning (Student Abstract). [pdf]
  • Elhadji Amadou Oury Diallo, Toshiharu Sugawara. AAAI 2020.
• Multi-View Deep Attention Network for Reinforcement Learning (Student Abstract). [pdf]
  • Yueyue Hu, Shiliang Sun, Xin Xu, Jing Zhao. AAAI 2020.
• Determining the Possibility of Transfer Learning in Deep Reinforcement Learning Using Grad-CAM (Student Abstract). [pdf]
  • Ho-Taek Joo, Kyung-Joong Kim. AAAI 2020.
• Selecting Portfolios Directly Using Recurrent Reinforcement Learning (Student Abstract). [pdf]
  • Lin Li. AAAI 2020.
• Gifting in Multi-Agent Reinforcement Learning (Student Abstract). [pdf]
  • Andrei Lupu, Doina Precup. AAAI 2020.
• ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). [pdf]
  • Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul. AAAI 2020.
• Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract). [pdf]
  • Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor. AAAI 2020.
• Optimal Exploration Algorithm of Multi-Agent Reinforcement Learning Methods (Student Abstract). [pdf]
  • Qisheng Wang, Qichao Wang, Xiao Li. AAAI 2020.
• Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract). [pdf]
  • Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato. AAAI 2020.

• Unsupervised Reinforcement Learning. [pdf]
  • Sergey Levine. AAMAS 2020.
• Multi Type Mean Field Reinforcement Learning. [pdf]
  • Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde. AAMAS 2020.
• Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. [pdf]
  • Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White. AAMAS 2020.
• Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments. [pdf]
  • Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich. AAMAS 2020.
• Cautious Reinforcement Learning with Logical Constraints. [pdf]
  • Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening. AAMAS 2020.
• Inducing Cooperation through Reward Reshaping based on Peer Evaluations in Deep Multi-Agent Reinforcement Learning. [pdf]
  • David Earl Hostallero, Daewoo Kim, Sangwoo Moon, Kyunghwan Son, Wan Ju Kang, Yung Yi. AAMAS 2020.
• Snooping Attacks on Deep Reinforcement Learning. [pdf]
  • Matthew Inkawhich, Yiran Chen, Hai Helen Li. AAMAS 2020.
• A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry. [pdf]
  • Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf, Jenna M. Reinen, Irina Rish. AAMAS 2020.
• Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration. [pdf]
  • Anji Liu, Yitao Liang, Guy Van den Broeck. AAMAS 2020.
• Gifting in Multi-Agent Reinforcement Learning. [pdf]
  • Andrei Lupu, Doina Precup. AAMAS 2020.
• Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning. [pdf]
  • Xueguang Lyu, Christopher Amato. AAMAS 2020.
• Feudal Multi-Agent Deep Reinforcement Learning for Traffic Signal Control. [pdf]
  • Jinming Ma, Feng Wu. AAMAS 2020.
• Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. [pdf]
  • Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo. AAMAS 2020.
• Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing. [pdf]
  • Declan Oller, Tobias Glasmachers, Giuseppe Cuccu. AAMAS 2020.
• Yesterday's Reward is Today's Punishment: Contrast Effects in Human Feedback to Reinforcement Learning Agents. [pdf]
  • Divya Ramesh, Anthony Z. Liu, Andres J. Echeverria, Jean Y. Song, Nicholas R. Waytowich, Walter S. Lasecki. AAMAS 2020.
• Playing Games in the Dark: An Approach for Cross-Modality Transfer in Reinforcement Learning. [pdf]
  • Rui Silva, Miguel Vasco, Francisco S. Melo, Ana Paiva, Manuela Veloso. AAMAS 2020.
• Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management. [pdf]
  • Arambam James Singh, Akshat Kumar, Hoong Chuin Lau. AAMAS 2020.
• Strategyproof Reinforcement Learning for Online Resource Allocation. [pdf]
  • Sebastian Stein, Mateusz Ochal, Ioana-Adriana Moisoiu, Enrico H. Gerding, Raghu K. Ganti, Ting He, Tom La Porta. AAMAS 2020.
• Can Agents Learn by Analogy?: An Inferable Model for PAC Reinforcement Learning. [pdf]
  • Yanchao Sun, Furong Huang. AAMAS 2020.
• Learning Context-aware Task Reasoning for Efficient Meta Reinforcement Learning. [pdf]
  • Haozhe Wang, Jiale Zhou, Xuming He. AAMAS 2020.
• A Performance-Based Start State Curriculum Framework for Reinforcement Learning. [pdf]
  • Jan Wöhlke, Felix Schmitt, Herke van Hoof. AAMAS 2020.
• Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery. [pdf]
  • Jiachen Yang, Igor Borovikov, Hongyuan Zha. AAMAS 2020.
• Deep Residual Reinforcement Learning. [pdf]
  • Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson. AAMAS 2020.
• Competitive and Cooperative Heterogeneous Deep Reinforcement Learning. [pdf]
  • Han Zheng, Jing Jiang, Pengfei Wei, Guodong Long, Chengqi Zhang. AAMAS 2020.
• Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning. [pdf]
  • Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, P. M. Krafft, Esteban Moro, Alex Pentland. AAMAS 2020.
• Evolving Meta-Level Reasoning with Reinforcement Learning and A for Coordinated Multi-Agent Path-planning.* [pdf]
  • Mona Alshehri, Napoleon H. Reyes, Andre L. C. Barczak. AAMAS 2020.
• Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning. [pdf]
  • Andrea Baisero, Christopher Amato. AAMAS 2020.
• Reinforcement Learning Dynamics in the Infinite Memory Limit. [pdf]
  • Wolfram Barfuss. AAMAS 2020.
• A New Framework for Multi-Agent Reinforcement Learning - Centralized Training and Exploration with Decentralized Execution via Policy Distillation. [pdf]
  • Gang Chen. AAMAS 2020.
• Social Structure Emergence: A Multi-agent Reinforcement Learning Framework for Relationship Building. [pdf]
  • Yang Chen, Jiamou Liu, He Zhao, Hongyi Su. AAMAS 2020.
• Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation. [pdf]
  • Guohui Ding, Joewie J. Koh, Kelly Merckaert, Bram Vanderborght, Marco M. Nicotra, Christoffer Heckman, Alessandro Roncone, Lijun Chen. AAMAS 2020.
• Decomposed Deep Reinforcement Learning for Robotic Control. [pdf]
  • Yinzhao Dong, Chao Yu, Paul Weng, Ahmed Maustafa, Hui Cheng, Hongwei Ge. AAMAS 2020.
• Cluster-Based Social Reinforcement Learning. [pdf]
  • Mahak Goindani, Jennifer Neville. AAMAS 2020.
• Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables. [pdf]
  • Nate Gruver, Jiaming Song, Mykel J. Kochenderfer, Stefano Ermon. AAMAS 2020.
• Networked Multi-Agent Reinforcement Learning with Emergent Communication. [pdf]
  • Shubham Gupta, Rishi Hazra, Ambedkar Dukkipati. AAMAS 2020.
• Mastering Basketball With Deep Reinforcement Learning: An Integrated Curriculum Training Approach. [pdf]
  • Hangtian Jia, Chunxu Ren, Yujing Hu, Yingfeng Chen, Tangjie Lv, Changjie Fan, Hongyao Tang, Jianye Hao. AAMAS 2020.
• Deep Reinforcement Learning for Market Making. [pdf]
  • Pankaj Kumar. AAMAS 2020.
• Genetic Deep Reinforcement Learning for Mapless Navigation. [pdf]
  • Enrico Marchesini, Alessandro Farinelli. AAMAS 2020.
• Multi-Vehicle Mixed Reality Reinforcement Learning for Autonomous Multi-Lane Driving. [pdf]
  • Rupert Mitchell, Jenny Fletcher, Jacopo Panerati, Amanda Prorok. AAMAS 2020.
• Mini-batch Bayesian Inverse Reinforcement Learning for Multiple Dynamics. [pdf]
  • Yusuke Nakata, Sachiyo Arai. AAMAS 2020.
• Vulcano: Operational Fire Suppression Management Using Deep Reinforcement Learning. [pdf]
  • Cristobal Pais. AAMAS 2020.
• Hierarchical Reinforcement Learning with Integrated Discovery of Salient Subgoals. [pdf]
  • Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan. AAMAS 2020.
• Robust Market Making via Adversarial Reinforcement Learning. [pdf]
  • Thomas Spooner, Rahul Savani. AAMAS 2020.
• Efficient Deep Reinforcement Learning through Policy Transfer. [pdf]
  • Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, Yujing Hu, Yingfeng Chen, Changjie Fan, Weixun Wang, Zhaodong Wang, Jiajie Peng. AAMAS 2020.
• Learning to Cooperate: Application of Deep Reinforcement Learning for Online AGV Path Finding. [pdf]
  • Yi Zhang, Yu Qian, Yichen Yao, Haoyuan Hu, Yinghui Xu. AAMAS 2020.
• Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games. [pdf]
  • Yijie Zhang, Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé. AAMAS 2020.
• Integrating Independent and Centralized Multi-agent Reinforcement Learning for Traffic Signal Network Optimization. [pdf]
  • Zhi Zhang, Jiachen Yang, Hongyuan Zha. AAMAS 2020.
• Hierarchical and Non-Hierarchical Multi-Agent Interactions Based on Unity Reinforcement Learning. [pdf]
  • Zehong Cao, Kaichiu Wong, Quan Bai, Chin-Teng Lin. AAMAS 2020.
• A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning. [pdf]
  • Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor. AAMAS 2020.
• Reinforcement Learning Algorithms for Autonomous Adaptive Agents. [pdf]
  • Sindhu Padakandla. AAMAS 2020.

• Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. [pdf]
  • Yu Chen, Lingfei Wu, Mohammed J. Zaki. ICLR 2020.
• Sharing Knowledge in Multi-Task Deep Reinforcement Learning. [pdf]
  • Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters. ICLR 2020.
• On the Weaknesses of Reinforcement Learning for Neural Machine Translation. [pdf]
  • Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend. ICLR 2020.
• SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards. [pdf]
  • Siddharth Reddy, Anca D. Dragan, Sergey Levine. ICLR 2020.
• Observational Overfitting in Reinforcement Learning. [pdf]
  • Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur. ICLR 2020.
• Multi-agent Reinforcement Learning for Networked System Control. [pdf]
  • Tianshu Chu, Sandeep Chinchali, Sachin Katti. ICLR 2020.
• Learning the Arrow of Time for Problems in Reinforcement Learning. [pdf]
  • Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio. ICLR 2020.
• Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. [pdf]
  • Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio. ICLR 2020.
• Exploration in Reinforcement Learning with Deep Covering Options. [pdf]
  • Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Dimitri Konidaris. ICLR 2020.
• Adversarial Policies: Attacking Deep Reinforcement Learning. [pdf]
  • Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell. ICLR 2020.
• Population-Guided Parallel Policy Search for Reinforcement Learning. [pdf]
  • Whiyoung Jung, Giseung Park, Youngchul Sung. ICLR 2020.
• Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents. [pdf]
  • Christian Rupprecht, Cyril Ibrahim, Christopher J. Pal. ICLR 2020.
• Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning. [pdf]
  • Gil Lederman, Markus N. Rabe, Sanjit Seshia, Edward A. Lee. ICLR 2020.
• AMRL: Aggregated Memory For Reinforcement Learning. [pdf]
  • Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann. ICLR 2020.
• CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning. [pdf]
  • Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, Hongyuan Zha. ICLR 2020.
• Graph Constrained Reinforcement Learning for Natural Language Action Spaces. [pdf]
  • Prithviraj Ammanabrolu, Matthew J. Hausknecht. ICLR 2020.
• Composing Task-Agnostic Policies with Deep Reinforcement Learning. [pdf]
  • Ahmed Hussain Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip. ICLR 2020.
• Single Episode Policy Transfer in Reinforcement Learning. [pdf]
  • Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol. ICLR 2020.
• Robust Reinforcement Learning for Continuous Control with Model Misspecification. [pdf]
  • Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy A. Mann, Martin A. Riedmiller. ICLR 2020.
• Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. [pdf]
  • Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou. ICLR 2020.
• Graph Convolutional Reinforcement Learning. [pdf]
  • Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu. ICLR 2020.
• Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. [pdf]
  • Akanksha Atrey, Kaleigh Clary, David D. Jensen. ICLR 2020.
• Thinking While Moving: Deep Reinforcement Learning with Concurrent Control. [pdf]
  • Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog. ICLR 2020.
• Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning. [pdf]
  • Qian Long, Zihan Zhou, Abhinav Gupta, Fei Fang, Yi Wu, Xiaolong Wang. ICLR 2020.
• Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning. [pdf]
  • Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee. ICLR 2020.
• Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control. [pdf]
  • Tsui-Wei Weng, Krishnamurthy (Dj) Dvijotham, Jonathan Uesato, Kai Xiao, Sven Gowal, Robert Stanforth, Pushmeet Kohli. ICLR 2020.
• Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. [pdf]
  • Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin A. Riedmiller. ICLR 2020.
• Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies. [pdf]
  • Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Honglak Lee. ICLR 2020.
• Discriminative Particle Filter Reinforcement Learning for Complex Partial observations. [pdf]
  • Xiao Ma, Péter Karkus, David Hsu, Wee Sun Lee, Nan Ye. ICLR 2020.
• Episodic Reinforcement Learning with Associative Memory. [pdf]
  • Guangxiang Zhu, Zichuan Lin, Guangwen Yang, Chongjie Zhang. ICLR 2020.
• Sub-policy Adaptation for Hierarchical Reinforcement Learning. [pdf]
  • Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel. ICLR 2020.
• Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? [pdf]
  • Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang. ICLR 2020.
• Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning. [pdf]
  • Hengyuan Hu, Jakob N. Foerster. ICLR 2020.
• Behaviour Suite for Reinforcement Learning. [pdf]
  • Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt. ICLR 2020.
• Model Based Reinforcement Learning for Atari. [pdf]
  • Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski. ICLR 2020.
• Measuring the Reliability of Reinforcement Learning Algorithms. [pdf]
  • Stephanie C. Y. Chan, Samuel Fishman, Anoop Korattikara, John F. Canny, Sergio Guadarrama. ICLR 2020.
• The Ingredients of Real World Robotic Reinforcement Learning. [pdf]
  • Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine. ICLR 2020.
• Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning. [pdf]
  • Dexter R. R. Scobee, S. Shankar Sastry. ICLR 2020.
• Improving Generalization in Meta Reinforcement Learning using Learned Objectives. [pdf]
  • Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber. ICLR 2020.
• Making Sense of Reinforcement Learning and Probabilistic Inference. [pdf]
  • Brendan O'Donoghue, Ian Osband, Catalin Ionescu. ICLR 2020.
• Harnessing Structures for Value-Based Planning and Reinforcement Learning. [pdf]
  • Yuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabi. ICLR 2020.
• Causal Discovery with Reinforcement Learning. [pdf]
  • Shengyu Zhu, Ignavier Ng, Zhitang Chen. ICLR 2020.

• An Optimistic Perspective on Offline Reinforcement Learning. [pdf]
  • Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi. ICML 2020.
• Discount Factor as a Regularizer in Reinforcement Learning. [pdf]
  • Ron Amit, Ron Meir, Kamil Ciosek. ICML 2020.
• Model-Based Reinforcement Learning with Value-Targeted Regression. [pdf]
  • Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin Yang. ICML 2020.
• Provable Self-Play Algorithms for Competitive Reinforcement Learning. [pdf]
  • Yu Bai, Chi Jin. ICML 2020.
• Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters. [pdf]
  • Subho S. Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar K. Iyer. ICML 2020.
• Tightening Exploration in Upper Confidence Reinforcement Learning. [pdf]
  • Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi. ICML 2020.
• Description Based Text Classification with Reinforcement Learning. [pdf]
  • Duo Chai, Wei Wu, Qinghong Han, Fei Wu, Jiwei Li. ICML 2020.
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. [pdf]
  • Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine. ICML 2020.
• Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism. [pdf]
  • Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu. ICML 2020.
• Leveraging Procedural Generation to Benchmark Reinforcement Learning. [pdf]
  • Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman. ICML 2020.
• On the Expressivity of Neural Networks for Deep Reinforcement Learning. [pdf]
  • Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma. ICML 2020.
• Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location. [pdf]
  • Rasheed el-Bouri, David W. Eyre, Peter J. Watkinson, Tingting Zhu, David A. Clifton. ICML 2020.
• Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions. [pdf]
  • Sinong Geng, Houssam Nassif, Carlos A. Manzanares, A. Max Reppen, Ronnie Sircar. ICML 2020.
• Representations for Stable Off-Policy Reinforcement Learning. [pdf]
  • Dibya Ghosh, Marc G. Bellemare. ICML 2020.
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. [pdf]
  • Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez. ICML 2020.
• Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. [pdf]
  • Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam M. J. Thomas, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio. ICML 2020.
• Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. [pdf]
  • Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar. ICML 2020.
• A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits. [pdf]
  • Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu. ICML 2020.
• Generalization to New Actions in Reinforcement Learning. [pdf]
  • Ayush Jain, Andrew Szot, Joseph J. Lim. ICML 2020.
• Reward-Free Exploration for Reinforcement Learning. [pdf]
  • Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu. ICML 2020.
• Evaluating the Performance of Reinforcement Learning Algorithms. [pdf]
  • Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas. ICML 2020.
• Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning. [pdf]
  • Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar. ICML 2020.
• Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. [pdf]
  • Nathan Kallus, Masatoshi Uehara. ICML 2020.
• What can I do here? A Theory of Affordances in Reinforcement Learning. [pdf]
  • Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup. ICML 2020.
• CURL: Contrastive Unsupervised Representations for Reinforcement Learning. [pdf]
  • Michael Laskin, Aravind Srinivas, Pieter Abbeel. ICML 2020.
• Batch Reinforcement Learning with Hyperparameter Gradients. [pdf]
  • Byung-Jun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-Eung Kim. ICML 2020.
• Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning. [pdf]
  • Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin. ICML 2020.
• Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning. [pdf]
  • Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard B. Fair. ICML 2020.
• Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. [pdf]
  • Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer. ICML 2020.
• Stochastically Dominant Distributional Reinforcement Learning. [pdf]
  • John D. Martin, Michal Lyskawinski, Xiaohu Li, Brendan J. Englot. ICML 2020.
• Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning. [pdf]
  • Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli. ICML 2020.
• Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. [pdf]
  • Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford. ICML 2020.
• Can Increasing Input Dimensionality Improve Deep Reinforcement Learning? [pdf]
  • Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, Daniel Nikovski. ICML 2020.
• Stabilizing Transformers for Reinforcement Learning. [pdf]
  • Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell. ICML 2020.
• Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning. [pdf]
  • Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav S. Sukhatme, Vladlen Koltun. ICML 2020.
• Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning. [pdf]
  • Silviu Pitis, Harris Chan, Stephen Zhao, Bradly C. Stadie, Jimmy Ba. ICML 2020.
• Skew-Fit: State-Covering Self-Supervised Reinforcement Learning. [pdf]
  • Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine. ICML 2020.
• A Game Theoretic Framework for Model Based Reinforcement Learning. [pdf]
  • Aravind Rajeswaran, Igor Mordatch, Vikash Kumar. ICML 2020.
• Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. [pdf]
  • Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla. ICML 2020.
• Deep Reinforcement Learning with Robust and Smooth Policy. [pdf]
  • Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao. ICML 2020.
• Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards. [pdf]
  • Umer Siddique, Paul Weng, Matthieu Zimmer. ICML 2020.
• Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. [pdf]
  • Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato. ICML 2020.
• Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. [pdf]
  • Adam Stooke, Joshua Achiam, Pieter Abbeel. ICML 2020.
• Reinforcement Learning for Integer Programming: Learning to Cut. [pdf]
  • Yunhao Tang, Shipra Agrawal, Yuri Faenza. ICML 2020.
• Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. [pdf]
  • Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens. ICML 2020.
• No-Regret Exploration in Goal-Oriented Reinforcement Learning. [pdf]
  • Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric. ICML 2020.
• Sequential Transfer in Reinforcement Learning with a Generative Model. [pdf]
  • Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli. ICML 2020.
• Multi-step Greedy Reinforcement Learning Algorithms. [pdf]
  • Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh. ICML 2020.
• Private Reinforcement Learning with PAC and Regret Guarantees. [pdf]
  • Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu. ICML 2020.
• Safe Reinforcement Learning in Constrained Markov Decision Processes. [pdf]
  • Akifumi Wachi, Yanan Sui. ICML 2020.
• ROMA: Multi-Agent Reinforcement Learning with Emergent Roles. [pdf]
  • Tonghan Wang, Heng Dong, Victor R. Lesser, Chongjie Zhang. ICML 2020.
• Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions. [pdf]
  • Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, Kenneth O. Stanley. ICML 2020.
• Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning. [pdf]
  • Lingxiao Wang, Zhuoran Yang, Zhaoran Wang. ICML 2020.
• Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. [pdf]
  • Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain. ICML 2020.
• Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control. [pdf]
  • Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik. ICML 2020.
• Q-value Path Decomposition for Deep Multiagent Reinforcement Learning. [pdf]
  • Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen, Yujing Hu, Changjie Fan, Zhongyu Wei. ICML 2020.
• Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. [pdf]
  • Lin Yang, Mengdi Wang. ICML 2020.
• Data Valuation using Reinforcement Learning. [pdf]
  • Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister. ICML 2020.
• Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach. [pdf]
  • Junzhe Zhang. ICML 2020.
• Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. [pdf]
  • Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman. ICML 2020.
• Adaptive Reward-Poisoning Attacks against Reinforcement Learning. [pdf]
  • Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu. ICML 2020.

• Deep Merging: Vehicle Merging Controller Based on Deep Reinforcement Learning with Embedding Network. [pdf]
  • Ippei Nishitani, Hao Yang, Rui Guo, Shalini Keshavamurthy, Kentaro Oguchi. ICRA 2020.
• Reinforcement Learning for Adaptive Illumination with X-rays. [pdf]
  • Jean-Raymond Betterton, Daniel Ratner, Samuel Webb, Mykel J. Kochenderfer. ICRA 2020.
• Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning. [pdf]
  • Tianyu Li, Nathan O. Lambert, Roberto Calandra, Franziska Meier, Akshara Rai. ICRA 2020.
• Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning. [pdf]
  • Ruihua Han, Shengduo Chen, Qi Hao. ICRA 2020.
• Knowledge-Guided Reinforcement Learning Control for Robotic Lower Limb Prosthesis. [pdf]
  • Xiang Gao, Jennie Si, Yue Wen, Minhan Li, He Helen Huang. ICRA 2020.
• Feedback Linearization for Uncertain Systems via Reinforcement Learning. [pdf]
  • Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin. ICRA 2020.
• Reinforcement Learning Based Manipulation Skill Transferring for Robot-assisted Minimally Invasive Surgery. [pdf]
  • Hang Su, Yingbai Hu, Zhijun Li, Alois C. Knoll, Giancarlo Ferrigno, Elena De Momi. ICRA 2020.
• Fine-Grained Driving Behavior Prediction via Context-Aware Multi-Task Inverse Reinforcement Learning. [pdf]
  • Kentaro Nishi, Masamichi Shimosaka. ICRA 2020.
• GA3C Reinforcement Learning for Surgical Steerable Catheter Path Planning. [pdf]
  • Alice Segato, Luca Sestini, Antonella Castellano, Elena De Momi. ICRA 2020.
• Grasp for Stacking via Deep Reinforcement Learning. [pdf]
  • Junhao Zhang, Wei Zhang, Ran Song, Lin Ma, Yibin Li. ICRA 2020.
• Meta Reinforcement Learning for Sim-to-real Domain Adaptation. [pdf]
  • Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki. ICRA 2020.
• DeepRacer: Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning. [pdf]
  • Bharathan Balaji, Sunil Mallya, Sahika Genc, Saurabh Gupta, Leo Dirac, Vineet Khare, Gourav Roy, Tao Sun, Yunzhe Tao, Brian Townsend, Eddie Calleja, Sunil Muralidhara, Dhanasekar Karuppasamy. ICRA 2020.
• Imitative Reinforcement Learning Fusing Vision and Pure Pursuit for Self-driving. [pdf]
  • Mingxing Peng, Zhihao Gong, Chen Sun, Long Chen, Dongpu Cao. ICRA 2020.
• Fast Adaptation of Deep Reinforcement Learning-Based Navigation Skills to Human Preference. [pdf]
  • Jinyoung Choi, Christopher R. Dance, Jung-Eun Kim, Kyungsik Park, Jaehun Han, Joonho Seo, Minsu Kim. ICRA 2020.
• A Model-Based Reinforcement Learning and Correction Framework for Process Control of Robotic Wire Arc Additive Manufacturing. [pdf]
  • Audelia Gumarus Dharmawan, Yi Xiong, Shaohui Foong, Gim Song Soh. ICRA 2020.
• Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning. [pdf]
  • Richard Li, Allan Jabri, Trevor Darrell, Pulkit Agrawal. ICRA 2020.
• Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving. [pdf]
  • Maria Hügle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker. ICRA 2020.
• Dynamic Cloth Manipulation with Deep Reinforcement Learning. [pdf]
  • Rishabh Jangir, Guillem Alenyà, Carme Torras. ICRA 2020.
• Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning. [pdf]
  • Mohamed El-Shamouty, Xinyang Wu, Shanqi Yang, Marcel Albus, Marco F. Huber. ICRA 2020.
• Local Policy Optimization for Trajectory-Centric Reinforcement Learning. [pdf]
  • Patrik Kolaric, Devesh K. Jha, Arvind U. Raghunathan, Frank L. Lewis, Mouhacine Benosman, Diego Romeres, Daniel Nikovski. ICRA 2020.
• Driving in Dense Traffic with Model-Free Reinforcement Learning. [pdf]
  • Dhruv Mauria Saxena, Sangjae Bae, Alireza Nakhaei, Kikuo Fujimura, Maxim Likhachev. ICRA 2020.
• Analyzing the Suitability of Cost Functions for Explaining and Imitating Human Driving Behavior based on Inverse Reinforcement Learning. [pdf]
  • Maximilian Naumann, Liting Sun, Wei Zhan, Masayoshi Tomizuka. ICRA 2020.
• Grasping Unknown Objects by Coupling Deep Reinforcement Learning, Generative Adversarial Networks, and Visual Servoing. [pdf]
  • Ole-Magnus Pedersen, Ekrem Misimi, François Chaumette. ICRA 2020.
• Simulation-Based Reinforcement Learning for Real-World Autonomous Driving. [pdf]
  • Blazej Osinski, Adam Jakubowski, Pawel Ziecina, Piotr Milos, Christopher Galias, Silviu Homoceanu, Henryk Michalewski. ICRA 2020.
• Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics. [pdf]
  • Shuo Li, Osbert Bastani. ICRA 2020.
• Learn-to-Recover: Retrofitting UAVs with Reinforcement Learning-Assisted Flight Control Under Cyber-Physical Attacks. [pdf]
  • Fan Fei, Zhan Tu, Dongyan Xu, Xinyan Deng. ICRA 2020.
• On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning. [pdf]
  • Ameya Pore, Gerardo Aragon-Camarasa. ICRA 2020.
• Interactive Reinforcement Learning with Inaccurate Feedback. [pdf]
  • Taylor A. Kessler Faulkner, Elaine Schaertl Short, Andrea Lockerd Thomaz. ICRA 2020.
• C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning. [pdf]
  • Taewoo Kim, Joo-Haeng Lee. ICRA 2020.
• Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search. [pdf]
  • Xiaojie Gao, Yueming Jin, Qi Dou, Pheng-Ann Heng. ICRA 2020.
• Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations. [pdf]
  • Qitong Gao, Miroslav Pajic, Michael M. Zavlanos. ICRA 2020.
• Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning. [pdf]
  • Guillermo A. Castillo, Bowen Weng, Wei Zhang, Ayonga Hereid. ICRA 2020.
• Data-Driven Reinforcement Learning for Walking Assistance Control of a Lower Limb Exoskeleton with Hemiplegic Patients. [pdf]
  • Zhinan Peng, Rui Luo, Rui Huang, Jiangping Hu, Kecheng Shi, Hong Cheng, Bijoy Kumar Ghosh. ICRA 2020.
• Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning. [pdf]
  • Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos, Miroslav Pajic. ICRA 2020.
• Dynamic Actor-Advisor Programming for Scalable Safe Reinforcement Learning. [pdf]
  • Lingwei Zhu, Yunduan Cui, Takamitsu Matsubara. ICRA 2020.
• Discrete Deep Reinforcement Learning for Mapless Navigation. [pdf]
  • Enrico Marchesini, Alessandro Farinelli. ICRA 2020.
• Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization. [pdf]
  • Matteo Turchetta, Andreas Krause, Sebastian Trimpe. ICRA 2020.
• Evaluating Adaptation Performance of Hierarchical Deep Reinforcement Learning. [pdf]
  • Neale Van Stolen, Seung Hyun Kim, Huy T. Tran, Girish Chowdhary. ICRA 2020.

• A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation. [pdf]
  • Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis. IJCAI 2020.
• Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach. [pdf]
  • Yujian Ye, Dawei Qiu, Jonathan Ward, Marcin Abram. IJCAI 2020.
• Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning. [pdf]
  • Man Luo, Wenzhe Zhang, Tianyou Song, Kun Li, Hongming Zhu, Bowen Du, Hongkai Wen. IJCAI 2020.
• Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning. [pdf]
  • Guojia Wan, Shirui Pan, Chen Gong, Chuan Zhou, Gholamreza Haffari. IJCAI 2020.
• Reinforcement Learning with Dynamic Boltzmann Softmax Updates. [pdf]
  • Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang. IJCAI 2020.
• Potential Driven Reinforcement Learning for Hard Exploration Tasks. [pdf]
  • Enmin Zhao, Shihong Deng, Yifan Zang, Yongxin Kang, Kai Li, Junliang Xing. IJCAI 2020.
• Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning. [pdf]
  • Dieqiao Feng, Carla P. Gomes, Bart Selman. IJCAI 2020.
• KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge. [pdf]
  • Peng Zhang, Jianye Hao, Weixun Wang, Hongyao Tang, Yi Ma, Yihai Duan, Yan Zheng. IJCAI 2020.
• Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning. [pdf]
  • Ishan Durugkar, Elad Liebman, Peter Stone. IJCAI 2020.
• Self-Attentional Credit Assignment for Transfer in Reinforcement Learning. [pdf]
  • Johan Ferret, Raphaël Marinier, Matthieu Geist, Olivier Pietquin. IJCAI 2020.
• I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations. [pdf]
  • Xufang Luo, Qi Meng, Di He, Wei Chen, Yunhong Wang. IJCAI 2020.
• Reinforcement Learning Framework for Deep Brain Stimulation Study. [pdf]
  • Dmitrii Krylov, Remi Tachet des Combes, Romain Laroche, Michael Rosenblum, Dmitry V. Dylov. IJCAI 2020.
• Constrained Policy Improvement for Efficient Reinforcement Learning. [pdf]
  • Elad Sarafian, Aviv Tamar, Sarit Kraus. IJCAI 2020.
• Independent Skill Transfer for Deep Reinforcement Learning. [pdf]
  • Qiangxing Tian, Guanchu Wang, Jinxin Liu, Donglin Wang, Yachen Kang. IJCAI 2020.
• Efficient Deep Reinforcement Learning via Adaptive Policy Transfer. [pdf]
  • Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, Yujing Hu, Yingfeng Chen, Changjie Fan, Weixun Wang, Wulong Liu, Zhaodong Wang, Jiajie Peng. IJCAI 2020.
• MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics. [pdf]
  • Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya. IJCAI 2020.
• I²HRL: Interactive Influence-based Hierarchical Reinforcement Learning. [pdf]
  • Rundong Wang, Runsheng Yu, Bo An, Zinovi Rabinovich. IJCAI 2020.
• Exploring Parameter Space with Structured Noise for Meta-Reinforcement Learning. [pdf]
  • Hui Xu, Chong Zhang, Jiaxing Wang, Deqiang Ouyang, Yu Zheng, Jie Shao. IJCAI 2020.
• DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling. [pdf]
  • Penghao Sun, Zehua Guo, Junchao Wang, Junfei Li, Julong Lan, Yuxiang Hu. IJCAI 2020.
• Semi-Markov Reinforcement Learning for Stochastic Resource Collection. [pdf]
  • Sebastian Schmoll, Matthias Schubert. IJCAI 2020.
• Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players. [pdf]
  • Yudong Luo, Oliver Schulte, Pascal Poupart. IJCAI 2020.
• Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning. [pdf]
  • Ruimin Shen, Yan Zheng, Jianye Hao, Zhaopeng Meng, Yingfeng Chen, Changjie Fan, Yang Liu. IJCAI 2020.
• A Two-level Reinforcement Learning Algorithm for Ambiguous Mean-variance Portfolio Selection Problem. [pdf]
  • Xin Huang, Duan Li. IJCAI 2020.
• Robust Market Making via Adversarial Reinforcement Learning. [pdf]
  • Thomas Spooner, Rahul Savani. IJCAI 2020.
• Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract). [pdf]
  • Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi. IJCAI 2020.
• On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract). [pdf]
  • Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau. IJCAI 2020.
• Towards High-Level Intrinsic Exploration in Reinforcement Learning. [pdf]
  • Nicolas Bougie, Ryutaro Ichise. IJCAI 2020.
• Generalized Representation Learning Methods for Deep Reinforcement Learning. [pdf]
  • Hanhua Zhu. IJCAI 2020.
• RLCard: A Platform for Reinforcement Learning in Card Games. [pdf]
  • Daochen Zha, Kwei-Herng Lai, Songyi Huang, Yuanpu Cao, Keerthana Reddy, Juan Vargas, Alex Nguyen, Ruzhe Wei, Junyu Guo, Xia Hu. IJCAI 2020.

• Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. [pdf]
  • Arthur Delarue, Ross Anderson, Christian Tjandraatmadja. NeurIPS 2020.
• Towards Playing Full MOBA Games with Deep Reinforcement Learning. [pdf]
  • Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu. NeurIPS 2020.
• Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. [pdf]
  • Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine. NeurIPS 2020.
• Discovering Reinforcement Learning Algorithms. [pdf]
  • Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver. NeurIPS 2020.
• Conservative Q-Learning for Offline Reinforcement Learning. [pdf]
  • Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine. NeurIPS 2020.
• Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. [pdf]
  • Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill. NeurIPS 2020.
• A Unifying View of Optimism in Episodic Reinforcement Learning. [pdf]
  • Gergely Neu, Ciara Pike-Burke. NeurIPS 2020.
• Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. [pdf]
  • Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Chi Xu. NeurIPS 2020.
• Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward. [pdf]
  • Guannan Qu, Yiheng Lin, Adam Wierman, Na Li. NeurIPS 2020.
• Near-Optimal Reinforcement Learning with Self-Play. [pdf]
  • Yu Bai, Chi Jin, Tiancheng Yu. NeurIPS 2020.
• Inverse Reinforcement Learning from a Gradient-based Learner. [pdf]
  • Giorgia Ramponi, Gianluca Drappo, Marcello Restelli. NeurIPS 2020.
• Weakly-Supervised Reinforcement Learning for Controllable Behavior. [pdf]
  • Lisa Lee, Ben Eysenbach, Ruslan Salakhutdinov, Shixiang Shane Gu, Chelsea Finn. NeurIPS 2020.
• Reinforcement Learning for Control with Multiple Frequencies. [pdf]
  • Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim. NeurIPS 2020.
• Adaptive Discretization for Model-Based Reinforcement Learning. [pdf]
  • Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Lee Yu. NeurIPS 2020.
• AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control. [pdf]
  • Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva. NeurIPS 2020.
• Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. [pdf]
  • Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh. NeurIPS 2020.
• MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. [pdf]
  • Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling. NeurIPS 2020.
• Munchausen Reinforcement Learning. [pdf]
  • Nino Vieillard, Olivier Pietquin, Matthieu Geist. NeurIPS 2020.
• Variational Policy Gradient Method for Reinforcement Learning with General Utilities. [pdf]
  • Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang. NeurIPS 2020.
• Multi-Task Reinforcement Learning with Soft Modularization. [pdf]
  • Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang. NeurIPS 2020.
• Independent Policy Gradient Methods for Competitive Reinforcement Learning. [pdf]
  • Constantinos Daskalakis, Dylan J. Foster, Noah Golowich. NeurIPS 2020.
• The Value Equivalence Principle for Model-Based Reinforcement Learning. [pdf]
  • Christopher Grimm, André Barreto, Satinder Singh, David Silver. NeurIPS 2020.
• Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? [pdf]
  • Qiwen Cui, Lin F. Yang. NeurIPS 2020.
• Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. [pdf]
  • Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang. NeurIPS 2020.
• Neurosymbolic Reinforcement Learning with Formally Verified Exploration. [pdf]
  • Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri. NeurIPS 2020.
• Multi-task Batch Reinforcement Learning with Metric Learning. [pdf]
  • Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik I. Christensen, Hao Su. NeurIPS 2020.
• Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes. [pdf]
  • Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao. NeurIPS 2020.
• The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. [pdf]
  • Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar. NeurIPS 2020.
• On Efficiency in Hierarchical Reinforcement Learning. [pdf]
  • Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh. NeurIPS 2020.
• Implicit Distributional Reinforcement Learning. [pdf]
  • Yuguang Yue, Zhendong Wang, Mingyuan Zhou. NeurIPS 2020.
• RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. [pdf]
  • Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas. NeurIPS 2020.
• Generalized Hindsight for Reinforcement Learning. [pdf]
  • Alexander C. Li, Lerrel Pinto, Pieter Abbeel. NeurIPS 2020.
• Improving Generalization in Reinforcement Learning with Mixture Regularization. [pdf]
  • Kaixin Wang, Bingyi Kang, Jie Shao, Jiashi Feng. NeurIPS 2020.
• Robust Reinforcement Learning via Adversarial training with Langevin Dynamics. [pdf]
  • Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh, Paul Rolland, Cheng Shi, Volkan Cevher. NeurIPS 2020.
• Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms. [pdf]
  • Pinar Ozisik, Philip S. Thomas. NeurIPS 2020.
• Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning. [pdf]
  • Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang. NeurIPS 2020.
• Self-Paced Deep Reinforcement Learning. [pdf]
  • Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen. NeurIPS 2020.
• Steady State Analysis of Episodic Reinforcement Learning. [pdf]
  • Bojun Huang. NeurIPS 2020.
• A Boolean Task Algebra for Reinforcement Learning. [pdf]
  • Geraud Nangue Tasse, Steven D. James, Benjamin Rosman. NeurIPS 2020.
• Model-based Adversarial Meta-Reinforcement Learning. [pdf]
  • Zichuan Lin, Garrett Thomas, Guangwen Yang, Tengyu Ma. NeurIPS 2020.
• Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. [pdf]
  • Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson. NeurIPS 2020.
• Robust Multi-Agent Reinforcement Learning with Model Uncertainty. [pdf]
  • Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar. NeurIPS 2020.
• Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning. [pdf]
  • Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht. NeurIPS 2020.
• Instance-based Generalization in Reinforcement Learning. [pdf]
  • Martín Bertrán, Natalia Martínez, Mariano Phielipp, Guillermo Sapiro. NeurIPS 2020.
• Task-agnostic Exploration in Reinforcement Learning. [pdf]
  • Xuezhou Zhang, Yuzhe Ma, Adish Singla. NeurIPS 2020.
• Online Decision Based Visual Tracking via Reinforcement Learning. [pdf]
  • Ke Song, Wei Zhang, Ran Song, Yibin Li. NeurIPS 2020.
• Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, Yuk Ying Chung. NeurIPS 2020.
• Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. [pdf]
  • Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang. NeurIPS 2020.
• Safe Reinforcement Learning via Curriculum Induction. [pdf]
  • Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal. NeurIPS 2020.
• Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. [pdf]
  • Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist. NeurIPS 2020.
• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. [pdf]
  • Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen. NeurIPS 2020.
• Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning. [pdf]
  • Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel. NeurIPS 2020.
• A Local Temporal Difference Code for Distributional Reinforcement Learning. [pdf]
  • Pablo Tano, Peter Dayan, Alexandre Pouget. NeurIPS 2020.
• Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. [pdf]
  • Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan. NeurIPS 2020.
• Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning. [pdf]
  • Sebastian Curi, Felix Berkenkamp, Andreas Krause. NeurIPS 2020.
• Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning. [pdf]
  • Jianda Chen, Shangyu Chen, Sinno Jialin Pan. NeurIPS 2020.
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control. [pdf]
  • Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye. NeurIPS 2020.
• Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition. [pdf]
  • Zihan Zhang, Yuan Zhou, Xiangyang Ji. NeurIPS 2020.
• Meta-Gradient Reinforcement Learning with an Objective Discovered Online. [pdf]
  • Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver. NeurIPS 2020.
• Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss. [pdf]
  • Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang. NeurIPS 2020.
• Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning. [pdf]
  • Julien Roy, Paul Barde, Félix G. Harvey, Derek Nowrouzezahrai, Chris Pal. NeurIPS 2020.
• Non-Crossing Quantile Regression for Distributional Reinforcement Learning. [pdf]
  • Fan Zhou, Jianing Wang, Xingdong Feng. NeurIPS 2020.
• Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games. [pdf]
  • Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang. NeurIPS 2020.
• Reinforcement Learning with Feedback Graphs. [pdf]
  • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan. NeurIPS 2020.
• Combining Deep Reinforcement Learning and Search for Imperfect-Information Games. [pdf]
  • Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong. NeurIPS 2020.
• Cooperative Heterogeneous Deep Reinforcement Learning. [pdf]
  • Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang. NeurIPS 2020.
• On Reward-Free Reinforcement Learning with Linear Function Approximation. [pdf]
  • Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov. NeurIPS 2020.
• Effective Diversity in Population Based Reinforcement Learning. [pdf]
  • Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts. NeurIPS 2020.
• Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting. [pdf]
  • Ziping Xu, Ambuj Tewari. NeurIPS 2020.
• BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning. [pdf]
  • Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith W. Ross. NeurIPS 2020.
• Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. [pdf]
  • Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu. NeurIPS 2020.
• DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. [pdf]
  • Aviral Kumar, Abhishek Gupta, Sergey Levine. NeurIPS 2020.
• Preference-based Reinforcement Learning with Finite-Time Guarantees. [pdf]
  • Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski. NeurIPS 2020.
• Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation. [pdf]
  • Aaron Sonabend W., Junwei Lu, Leo Anthony Celi, Tianxi Cai, Peter Szolovits. NeurIPS 2020.
• Accelerating Reinforcement Learning through GPU Atari Emulation. [pdf]
  • Steven Dalton, Iuri Frosio. NeurIPS 2020.
• Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. [pdf]
  • Jianzhun Du, Joseph Futoma, Finale Doshi-Velez. NeurIPS 2020.
• Reinforcement Learning with Augmented Data. [pdf]
  • Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas. NeurIPS 2020.
• Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. [pdf]
  • Yi Tian, Jian Qian, Suvrit Sra. NeurIPS 2020.
• Learning Retrospective Knowledge with Reverse Reinforcement Learning. [pdf]
  • Shangtong Zhang, Vivek Veeriah, Shimon Whiteson. NeurIPS 2020.
• TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search. [pdf]
  • Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul M. Zimmerman. NeurIPS 2020.
• Information-theoretic Task Selection for Meta-Reinforcement Learning. [pdf]
  • Ricardo Luna Gutierrez, Matteo Leonetti. NeurIPS 2020.
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations. [pdf]
  • Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane S. Boning, Cho-Jui Hsieh. NeurIPS 2020.
• POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. [pdf]
  • Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min. NeurIPS 2020.
• Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning. [pdf]
  • Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen. NeurIPS 2020.
• MOReL: Model-Based Offline Reinforcement Learning. [pdf]
  • Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims. NeurIPS 2020.
• On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems. [pdf]
  • Kaiqing Zhang, Bin Hu, Tamer Basar. NeurIPS 2020.
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. [pdf]
  • Nathan Kallus, Angela Zhou. NeurIPS 2020.
• Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret. [pdf]
  • Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie. NeurIPS 2020.
• Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes. [pdf]
  • Salman Habib, Allison Beemer, Jörg Kliewer. NeurIPS 2020.
• Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. [pdf]
  • Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang. NeurIPS 2020.