The proceedings of top conference in 2018 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.
- Related repository
- AAAI'2018
- AAMAS'2018
- ICLR'2018
- ICML'2018
- ICRA'2018
- IJCAI'2018
- NeurIPS'2018
Markdown format：
- **Paper Name**.
[[pdf](link)]
[[code](link)]
- Author 1, Author 2, and Author 3. *conference, year*.
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For any questions, feel free to contact me 📮.
- 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)
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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]
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