2018-Reinforcement-Learning-Conferences-Papers

The proceedings of top conference in 2018 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

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• Related repository
• AAAI'2018
• AAMAS'2018
• ICLR'2018
• ICML'2018
• ICRA'2018
• IJCAI'2018
• NeurIPS'2018

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Markdown format：

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

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Table of Contents

• AAAI Conference on Artificial Intelligence (AAAI'2018)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2018)
• International Conference on Learning Representations (ICLR'2018)
• International Conference on Machine Learning (ICML'2018)
• International Conference on Robotics and Automation (ICRA'2018)
• International Joint Conference on Artificial Intelligence (IJCAI'2018)
• Annual Conference on Neural Information Processing Systems (NeurIPS'2018)

AAAI Conference on Artificial Intelligence

• Cellular Network Traffic Scheduling With Deep Reinforcement Learning. [pdf]
  • Sandeep Chinchali, Pan Hu, Tianshu Chu, Manu Sharma, Manu Bansal, Rakesh Misra, Marco Pavone, Sachin Katti. AAAI 2018.
• Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example. [pdf]
  • Bar Hilleli, Ran El-Yaniv. AAAI 2018.
• A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents. [pdf]
  • Yueh-Hua Wu, Shou-De Lin. AAAI 2018.
• Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning. [pdf]
  • Hao-Cheng Kao, Kai-Fu Tang, Edward Y. Chang. AAAI 2018.
• Safe Reinforcement Learning via Shielding. [pdf]
  • Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu. AAAI 2018.
• Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. [pdf]
  • Daniel S. Brown, Scott Niekum. AAAI 2018.
• Teaching a Machine to Read Maps With Deep Reinforcement Learning. [pdf]
  • Gino Brunner, Oliver Richter, Yuyi Wang, Roger Wattenhofer. AAAI 2018.
• Distributional Reinforcement Learning With Quantile Regression. [pdf]
  • Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos. AAAI 2018.
• Multi-Step Reinforcement Learning: A Unifying Algorithm. [pdf]
  • Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton. AAAI 2018.
• OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning. [pdf]
  • Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup. AAAI 2018.
• Deep Reinforcement Learning That Matters. [pdf]
  • Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger. AAAI 2018.
• Rainbow: Combining Improvements in Deep Reinforcement Learning. [pdf]
  • Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver. AAAI 2018.
• PAC Reinforcement Learning With an Imperfect Model. [pdf]
  • Nan Jiang. AAAI 2018.
• Feature Engineering for Predictive Modeling Using Reinforcement Learning. [pdf]
  • Udayan Khurana, Horst Samulowitz, Deepak S. Turaga. AAAI 2018.
• An Optimal Online Method of Selecting Source Policies for Reinforcement Learning. [pdf]
  • Siyuan Li, Chongjie Zhang. AAAI 2018.
• Belief Reward Shaping in Reinforcement Learning. [pdf]
  • Ofir Marom, Benjamin Rosman. AAAI 2018.
• Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets. [pdf]
  • Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, Peter Vrancx, Hélène Plisnier, Ann Nowé. AAAI 2018.
• Action Branching Architectures for Deep Reinforcement Learning. [pdf]
  • Arash Tavakoli, Fabio Pardo, Petar Kormushev. AAAI 2018.
• BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems. [pdf]
  • Zachary C. Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng. AAAI 2018.
• Personalizing a Dialogue System With Transfer Reinforcement Learning. [pdf]
  • Kaixiang Mo, Yu Zhang, Shuangyin Li, Jiajun Li, Qiang Yang. AAAI 2018.
• MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning. [pdf]
  • Lei Wang, Dongxiang Zhang, Lianli Gao, Jingkuan Song, Long Guo, Heng Tao Shen. AAAI 2018.
• Learning to Extract Coherent Summary via Deep Reinforcement Learning. [pdf]
  • Yuxiang Wu, Baotian Hu. AAAI 2018.
• Large Scaled Relation Extraction With Reinforcement Learning. [pdf]
  • Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao. AAAI 2018.
• Reinforcement Learning for Relation Classification From Noisy Data. [pdf]
  • Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu. AAAI 2018.
• Learning Structured Representation for Text Classification via Reinforcement Learning. [pdf]
  • Tianyang Zhang, Minlie Huang, Li Zhao. AAAI 2018.
• Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning. [pdf]
  • Nathan Fulton, André Platzer. AAAI 2018.
• Phase-Parametric Policies for Reinforcement Learning in Cyclic Environments. [pdf]
  • Arjun Sharma, Kris M. Kitani. AAAI 2018.
• Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition. [pdf]
  • Tianshui Chen, Zhouxia Wang, Guanbin Li, Liang Lin. AAAI 2018.
• SAP: Self-Adaptive Proposal Model for Temporal Action Detection Based on Reinforcement Learning. [pdf]
  • Jingjia Huang, Nannan Li, Tao Zhang, Ge Li, Tiejun Huang, Wen Gao. AAAI 2018.
• Deep Reinforcement Learning for Unsupervised Video Summarization With Diversity-Representativeness Reward. [pdf]
  • Kaiyang Zhou, Yu Qiao, Tao Xiang. AAAI 2018.
• Towards Experienced Anomaly Detector Through Reinforcement Learning. [pdf]
  • Chengqiang Huang, Yulei Wu, Yuan Zuo, Ke Pei, Geyong Min. AAAI 2018.
• MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence. [pdf]
  • Lianmin Zheng, Jiacheng Yang, Han Cai, Ming Zhou, Weinan Zhang, Jun Wang, Yong Yu. AAAI 2018.

International Conference on Autonomous Agents and Multiagent Systems

• Market Making via Reinforcement Learning. [pdf]
  • Thomas Spooner, John Fearnley, Rahul Savani, Andreas Koukorinis. AAMAS 2018.
• Lenient Multi-Agent Deep Reinforcement Learning. [pdf]
  • Gregory Palmer, Karl Tuyls, Daan Bloembergen, Rahul Savani. AAMAS 2018.
• Valuing Knowledge, Information and Agency in Multi-agent Reinforcement Learning: A Case Study in Smart Buildings. [pdf]
  • Hussain Syed Kazmi, Johan A. K. Suykens, Johan Driesen. AAMAS 2018.
• A Stitch in Time - Autonomous Model Management via Reinforcement Learning. [pdf]
  • Elad Liebman, Eric Zavesky, Peter Stone. AAMAS 2018.
• Discovering Blind Spots in Reinforcement Learning. [pdf]
  • Ramya Ramakrishnan, Ece Kamar, Debadeepta Dey, Julie A. Shah, Eric Horvitz. AAMAS 2018.
• Object-Oriented Curriculum Generation for Reinforcement Learning. [pdf]
  • Felipe Leno da Silva, Anna Helena Reali Costa. AAMAS 2018.
• Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning. [pdf]
  • Xinlei Pan, Yilin Shen. AAMAS 2018.
• Multi-Agent Reinforcement Learning for Multi-Object Tracking. [pdf]
  • Pol Rosello, Mykel J. Kochenderfer. AAMAS 2018.
• Decentralized Reinforcement Learning Inspired by Multiagent Systems. [pdf]
  • Dhaval Adjodah. AAMAS 2018.
• Socially-Aware Reinforcement Learning for Personalized Human-Robot Interaction. [pdf]
  • Hannes Ritschel. AAMAS 2018.
• Guiding Reinforcement Learning Exploration Using Natural Language. [pdf]
  • Brent Harrison, Upol Ehsan, Mark O. Riedl. AAMAS 2018.
• Introspective Reinforcement Learning and Learning from Demonstration. [pdf]
  • Mao Li, Tim Brys, Daniel Kudenko. AAMAS 2018.
• Robust Deep Reinforcement Learning with Adversarial Attacks. [pdf]
  • Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, Girish Chowdhary. AAMAS 2018.
• Trial without Error: Towards Safe Reinforcement Learning via Human Intervention. [pdf]
  • William Saunders, Girish Sastry, Andreas Stuhlmüller, Owain Evans. AAMAS 2018.
• A Study of AI Population Dynamics with Million-agent Reinforcement Learning. [pdf]
  • Yaodong Yang, Lantao Yu, Yiwei Bai, Ying Wen, Weinan Zhang, Jun Wang. AAMAS 2018.
• Apprenticeship Bootstrapping: Inverse Reinforcement Learning in a Multi-Skill UAV-UGV Coordination Task. [pdf]
  • Hung The Nguyen, Matthew Garratt, Lam Thu Bui, Hussein A. Abbass. AAMAS 2018.

International Conference on Learning Representations

• Ask the Right Questions: Active Question Reformulation with Reinforcement Learning. [pdf]
  • Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang. ICLR 2018.
• Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. [pdf]
  • Wen Sun, J. Andrew Bagnell, Byron Boots. ICLR 2018.
• Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. [pdf]
  • Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli. ICLR 2018.
• The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. [pdf]
  • Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos. ICLR 2018.
• Reinforcement Learning Algorithm Selection. [pdf]
  • Romain Laroche, Raphaël Féraud. ICLR 2018.
• Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. [pdf]
  • Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine. ICLR 2018.
• Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. [pdf]
  • Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum. ICLR 2018.
• Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration. [pdf]
  • Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang. ICLR 2018.
• Learning Robust Rewards with Adverserial Inverse Reinforcement Learning. [pdf]
  • Justin Fu, Katie Luo, Sergey Levine. ICLR 2018.
• TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning. [pdf]
  • Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox. ICLR 2018.
• TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. [pdf]
  • Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson. ICLR 2018.
• Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback. [pdf]
  • Hal Daumé III, John Langford, Amr Sharaf. ICLR 2018.
• Neural Map: Structured Memory for Deep Reinforcement Learning. [pdf]
  • Emilio Parisotto, Ruslan Salakhutdinov. ICLR 2018.
• Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control. [pdf]
  • Glen Berseth, Cheng Xie, Paul Cernek, Michiel van de Panne. ICLR 2018.
• N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning. [pdf]
  • Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani. ICLR 2018.
• Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning. [pdf]
  • Tianmin Shu, Caiming Xiong, Richard Socher. ICLR 2018.
• Divide-and-Conquer Reinforcement Learning. [pdf]
  • Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine. ICLR 2018.

International Conference on Machine Learning

• State Abstractions for Lifelong Reinforcement Learning. [pdf]
  • David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman. ICML 2018.
• Policy and Value Transfer in Lifelong Reinforcement Learning. [pdf]
  • David Abel, Yuu Jinnai, Sophie Yue Guo, George Dimitri Konidaris, Michael L. Littman. ICML 2018.
• Lipschitz Continuity in Model-based Reinforcement Learning. [pdf]
  • Kavosh Asadi, Dipendra Misra, Michael L. Littman. ICML 2018.
• Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. [pdf]
  • André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos. ICML 2018.
• Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. [pdf]
  • John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine. ICML 2018.
• GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms. [pdf]
  • Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer. ICML 2018.
• Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. [pdf]
  • Dane S. Corneil, Wulfram Gerstner, Johanni Brea. ICML 2018.
• Mix & Match Agent Curricula for Reinforcement Learning. [pdf]
  • Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu. ICML 2018.
• Implicit Quantile Networks for Distributional Reinforcement Learning. [pdf]
  • Will Dabney, Georg Ostrovski, David Silver, Rémi Munos. ICML 2018.
• SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. [pdf]
  • Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song. ICML 2018.
• Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning. [pdf]
  • Thomas G. Dietterich, George Trimponias, Zhitang Chen. ICML 2018.
• Coordinated Exploration in Concurrent Reinforcement Learning. [pdf]
  • Maria Dimakopoulou, Benjamin Van Roy. ICML 2018.
• Beyond the One-Step Greedy Approach in Reinforcement Learning. [pdf]
  • Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor. ICML 2018.
• Automatic Goal Generation for Reinforcement Learning Agents. [pdf]
  • Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel. ICML 2018.
• Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. [pdf]
  • Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner. ICML 2018.
• Latent Space Policies for Hierarchical Reinforcement Learning. [pdf]
  • Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine. ICML 2018.
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. [pdf]
  • Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine. ICML 2018.
• Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning. [pdf]
  • Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Anthony Valenzano, Sheila A. McIlraith. ICML 2018.
• Deep Variational Reinforcement Learning for POMDPs. [pdf]
  • Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson. ICML 2018.
• Feedback-Based Tree Search for Reinforcement Learning. [pdf]
  • Daniel R. Jiang, Emmanuel Ekwedike, Han Liu. ICML 2018.
• Regret Minimization for Partially Observable Deep Reinforcement Learning. [pdf]
  • Peter H. Jin, Kurt Keutzer, Sergey Levine. ICML 2018.
• Continual Reinforcement Learning with Complex Synapses. [pdf]
  • Christos Kaplanis, Murray Shanahan, Claudia Clopath. ICML 2018.
• Hierarchical Imitation and Reinforcement Learning. [pdf]
  • Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III. ICML 2018.
• Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling. [pdf]
  • Kyowoon Lee, Sol-A. Kim, Jaesik Choi, Seong-Whan Lee. ICML 2018.
• RLlib: Abstractions for Distributed Reinforcement Learning. [pdf]
  • Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica. ICML 2018.
• End-to-end Active Object Tracking via Reinforcement Learning. [pdf]
  • Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang. ICML 2018.
• An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning. [pdf]
  • Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan. ICML 2018.
• Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control. [pdf]
  • Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski. ICML 2018.
• Time Limits in Reinforcement Learning. [pdf]
  • Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev. ICML 2018.
• Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? [pdf]
  • Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg. ICML 2018.
• Modeling Others using Oneself in Multi-Agent Reinforcement Learning. [pdf]
  • Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus. ICML 2018.
• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. [pdf]
  • Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson. ICML 2018.
• Structured Control Nets for Deep Reinforcement Learning. [pdf]
  • Mario Srouji, Jian Zhang, Ruslan Salakhutdinov. ICML 2018.
• Importance Weighted Transfer of Samples in Reinforcement Learning. [pdf]
  • Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli. ICML 2018.
• The Mirage of Action-Dependent Baselines in Reinforcement Learning. [pdf]
  • George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine. ICML 2018.
• Programmatically Interpretable Reinforcement Learning. [pdf]
  • Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri. ICML 2018.
• Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations. [pdf]
  • Xingyu Wang, Diego Klabjan. ICML 2018.
• Mean Field Multi-Agent Reinforcement Learning. [pdf]
  • Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun Wang. ICML 2018.
• Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. [pdf]
  • Andrea Zanette, Emma Brunskill. ICML 2018.
• Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. [pdf]
  • Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar. ICML 2018.

International Conference on Robotics and Automation

• Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning. [pdf]
  • Weihao Yuan, Johannes A. Stork, Danica Kragic, Michael Yu Wang, Kaiyu Hang. ICRA 2018.
• Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences. [pdf]
  • Robert Pinsler, Riad Akrour, Takayuki Osa, Jan Peters, Gerhard Neumann. ICRA 2018.
• Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis. [pdf]
  • Kun Li, Mrinal Rath, Joel W. Burdick. ICRA 2018.
• Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning. [pdf]
  • David Isele, Reza Rahimi, Akansel Cosgun, Kaushik Subramanian, Kikuo Fujimura. ICRA 2018.
• End-to-End Race Driving with Deep Reinforcement Learning. [pdf]
  • Maximilian Jaritz, Raoul de Charette, Marin Toromanoff, Etienne Perot, Fawzi Nashashibi. ICRA 2018.
• PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning. [pdf]
  • Aleksandra Faust, Kenneth Oslund, Oscar Ramirez, Anthony G. Francis, Lydia Tapia, Marek Fiser, James Davidson. ICRA 2018.
• OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World. [pdf]
  • Tu-Hoa Pham, Giovanni De Magistris, Ryuki Tachibana. ICRA 2018.
• Composable Deep Reinforcement Learning for Robotic Manipulation. [pdf]
  • Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine. ICRA 2018.
• Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning. [pdf]
  • Pinxin Long, Tingxiang Fan, Xinyi Liao, Wenxi Liu, Hao Zhang, Jia Pan. ICRA 2018.
• Tensegrity Robot Locomotion Under Limited Sensory Inputs via Deep Reinforcement Learning. [pdf]
  • Jianlan Luo, Riley Edmunds, Franklin Rice, Alice M. Agogino. ICRA 2018.
• Applying Asynchronous Deep Classification Networks and Gaming Reinforcement Learning-Based Motion Planners to Mobile Robots. [pdf]
  • Gilhyun Ryou, Youngwoo Sim, Seong Ho Yeon, Sangok Seok. ICRA 2018.
• Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning. [pdf]
  • Linhai Xie, Sen Wang, Stefano Rosa, Andrew Markham, Niki Trigoni. ICRA 2018.
• Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. [pdf]
  • Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine. ICRA 2018.
• Overcoming Exploration in Reinforcement Learning with Demonstrations. [pdf]
  • Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel. ICRA 2018.
• Cross-Domain Transfer in Reinforcement Learning Using Target Apprentice. [pdf]
  • Girish Joshi, Girish Chowdhary. ICRA 2018.
• Intent-Aware Multi-Agent Reinforcement Learning. [pdf]
  • Siyuan Qi, Song-Chun Zhu. ICRA 2018.
• Improving Model-Based Balance Controllers Using Reinforcement Learning and Adaptive Sampling. [pdf]
  • Visak C. V. Kumar, Sehoon Ha, Katsu Yamane. ICRA 2018.
• Deep Reinforcement Learning Supervised Autonomous Exploration in Office Environments. [pdf]
  • Delong Zhu, Tingguang Li, Danny Ho, Chaoqun Wang, Max Q.-H. Meng. ICRA 2018.
• Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning. [pdf]
  • Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine. ICRA 2018.
• Model-Based Probabilistic Pursuit via Inverse Reinforcement Learning. [pdf]
  • Florian Shkurti, Nikhil Kakodkar, Gregory Dudek. ICRA 2018.
• Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. [pdf]
  • Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine. ICRA 2018.
• EmoRL: Continuous Acoustic Emotion Classification Using Deep Reinforcement Learning. [pdf]
  • Egor Lakomkin, Mohammad-Ali Zamani, Cornelius Weber, Sven Magg, Stefan Wermter. ICRA 2018.
• Reinforcement Learning of Depth Stabilization with a Micro Diving Agent. [pdf]
  • Gerrit Brinkmann, Wallace Moreira Bessa, Daniel-André Duecker, Edwin Kreuzer, Eugen Solowjow. ICRA 2018.
• Reinforcement Learning for 4-Finger-Gripper Manipulation. [pdf]
  • Marco Ojer De Andres, M. Mahdi Ghazaei Ardakani, Anders Robertsson. ICRA 2018.
• Data-driven Construction of Symbolic Process Models for Reinforcement Learning. [pdf]
  • Erik Derner, Jirí Kubalík, Robert Babuska. ICRA 2018.

International Joint Conference on Artificial Intelligence

• Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach. [pdf]
  • Bo Yang, Min Liu. IJCAI 2018.
• StackDRL: Stacked Deep Reinforcement Learning for Fine-grained Visual Categorization. [pdf]
  • Xiangteng He, Yuxin Peng, Junjie Zhao. IJCAI 2018.
• Multi-Level Policy and Reward Reinforcement Learning for Image Captioning. [pdf]
  • Anan Liu, Ning Xu, Hanwang Zhang, Weizhi Nie, Yuting Su, Yongdong Zhang. IJCAI 2018.
• Master-Slave Curriculum Design for Reinforcement Learning. [pdf]
  • Yuechen Wu, Wei Zhang, Ke Song. IJCAI 2018.
• Cross-modal Bidirectional Translation via Reinforcement Learning. [pdf]
  • Jinwei Qi, Yuxin Peng. IJCAI 2018.
• Exploration by Distributional Reinforcement Learning. [pdf]
  • Yunhao Tang, Shipra Agrawal. IJCAI 2018.
• Algorithms or Actions? A Study in Large-Scale Reinforcement Learning. [pdf]
  • Anderson Rocha Tavares, Sivasubramanian Anbalagan, Leandro Soriano Marcolino, Luiz Chaimowicz. IJCAI 2018.
• A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning. [pdf]
  • Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng, Gang Pan. IJCAI 2018.
• Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning. [pdf]
  • Haiyan Yin, Jianda Chen, Sinno Jialin Pan. IJCAI 2018.
• Learning to Design Games: Strategic Environments in Reinforcement Learning. [pdf]
  • Haifeng Zhang, Jun Wang, Zhiming Zhou, Weinan Zhang, Yin Wen, Yong Yu, Wenxin Li. IJCAI 2018.
• Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation. [pdf]
  • Guiliang Liu, Oliver Schulte. IJCAI 2018.
• Impression Allocation for Combating Fraud in E-commerce Via Deep Reinforcement Learning with Action Norm Penalty. [pdf]
  • Mengchen Zhao, Zhao Li, Bo An, Haifeng Lu, Yifan Yang, Chen Chu. IJCAI 2018.
• Extracting Action Sequences from Texts Based on Deep Reinforcement Learning. [pdf]
  • Wenfeng Feng, Hankz Hankui Zhuo, Subbarao Kambhampati. IJCAI 2018.
• Toward Diverse Text Generation with Inverse Reinforcement Learning. [pdf]
  • Zhan Shi, Xinchi Chen, Xipeng Qiu, Xuanjing Huang. IJCAI 2018.
• A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning. [pdf]
  • Ryuichi Takanobu, Minlie Huang, Zhongzhou Zhao, Feng-Lin Li, Haiqing Chen, Xiaoyan Zhu, Liqiang Nie. IJCAI 2018.
• PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making. [pdf]
  • Fangkai Yang, Daoming Lyu, Bo Liu, Steven Gustafson. IJCAI 2018.
• Autonomously Reusing Knowledge in Multiagent Reinforcement Learning. [pdf]
  • Felipe Leno da Silva, Matthew E. Taylor, Anna Helena Reali Costa. IJCAI 2018.
• Improving Reinforcement Learning with Human Input. [pdf]
  • Matthew E. Taylor. IJCAI 2018.
• Towards Sample Efficient Reinforcement Learning. [pdf]
  • Yang Yu. IJCAI 2018.

Annual Conference on Neural Information Processing Systems

• Evolution-Guided Policy Gradient in Reinforcement Learning. [pdf]
  • Shauharda Khadka, Kagan Tumer. NeurIPS 2018.
• Genetic-Gated Networks for Deep Reinforcement Learning. [pdf]
  • Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak. NeurIPS 2018.
• Fighting Boredom in Recommender Systems with Linear Reinforcement Learning. [pdf]
  • Romain Warlop, Alessandro Lazaric, Jérémie Mary. NeurIPS 2018.
• Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning. [pdf]
  • Ofir Marom, Benjamin Rosman. NeurIPS 2018.
• Meta-Gradient Reinforcement Learning. [pdf]
  • Zhongwen Xu, Hado van Hasselt, David Silver. NeurIPS 2018.
• Verifiable Reinforcement Learning via Policy Extraction. [pdf]
  • Osbert Bastani, Yewen Pu, Armando Solar-Lezama. NeurIPS 2018.
• Deep Reinforcement Learning of Marked Temporal Point Processes. [pdf]
  • Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez. NeurIPS 2018.
• Data-Efficient Hierarchical Reinforcement Learning. [pdf]
  • Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine. NeurIPS 2018.
• Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. [pdf]
  • Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor. NeurIPS 2018.
• Scalable Coordinated Exploration in Concurrent Reinforcement Learning. [pdf]
  • Maria Dimakopoulou, Ian Osband, Benjamin Van Roy. NeurIPS 2018.
• Lifelong Inverse Reinforcement Learning. [pdf]
  • Jorge A. Mendez, Shashank Shivkumar, Eric Eaton. NeurIPS 2018.
• Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making. [pdf]
  • Nishant Desai, Andrew Critch, Stuart J. Russell. NeurIPS 2018.
• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. [pdf]
  • Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine. NeurIPS 2018.
• Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents. [pdf]
  • Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, Jeff Clune. NeurIPS 2018.
• Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning. [pdf]
  • Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor. NeurIPS 2018.
• Meta-Reinforcement Learning of Structured Exploration Strategies. [pdf]
  • Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine. NeurIPS 2018.
• Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing. [pdf]
  • Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu. NeurIPS 2018.
• Unsupervised Video Object Segmentation for Deep Reinforcement Learning. [pdf]
  • Vikash Goel, Jameson Weng, Pascal Poupart. NeurIPS 2018.
• Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. [pdf]
  • Sungryull Sohn, Junhyuk Oh, Honglak Lee. NeurIPS 2018.
• REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis. [pdf]
  • Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Y. Chang. NeurIPS 2018.
• Constrained Cross-Entropy Method for Safe Reinforcement Learning. [pdf]
  • Min Wen, Ufuk Topcu. NeurIPS 2018.
• A Lyapunov-based Approach to Safe Reinforcement Learning. [pdf]
  • Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh. NeurIPS 2018.
• Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. [pdf]
  • Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee. NeurIPS 2018.
• Randomized Prior Functions for Deep Reinforcement Learning. [pdf]
  • Ian Osband, John Aslanides, Albin Cassirer. NeurIPS 2018.
• Reinforcement Learning of Theorem Proving. [pdf]
  • Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Miroslav Olsák. NeurIPS 2018.
• Exploration in Structured Reinforcement Learning. [pdf]
  • Jungseul Ok, Alexandre Proutière, Damianos Tranos. NeurIPS 2018.
• Distributed Multitask Reinforcement Learning with Quadratic Convergence. [pdf]
  • Rasul Tutunov, Dongho Kim, Haitham Bou-Ammar. NeurIPS 2018.
• Visual Reinforcement Learning with Imagined Goals. [pdf]
  • Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine. NeurIPS 2018.
• The Importance of Sampling inMeta-Reinforcement Learning. [pdf]
  • Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever. NeurIPS 2018.
• Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach. [pdf]
  • Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee. NeurIPS 2018.
• Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization. [pdf]
  • Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong. NeurIPS 2018.
• Reinforcement Learning for Solving the Vehicle Routing Problem. [pdf]
  • MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác. NeurIPS 2018.
• Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. [pdf]
  • Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee. NeurIPS 2018.
• Learning Temporal Point Processes via Reinforcement Learning. [pdf]
  • Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song. NeurIPS 2018.