The proceedings of top conference in 2017 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.
- Related repository
- AAAI'2017
- AAMAS'2017
- ICLR'2017
- ICML'2017
- IJCAI'2017
- NeurIPS'2017
Markdown format：
- **Paper Name**.
[[pdf](link)]
[[code](link)]
- Author 1, Author 2, and Author 3. *conference, year*.
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- AAAI Conference on Artificial Intelligence (AAAI'2017)
- International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2017)
- International Conference on Learning Representations (ICLR'2017)
- International Conference on Machine Learning (ICML'2017)
- International Conference on Robotics and Automation (ICRA'2017)
- International Joint Conference on Artificial Intelligence (IJCAI'2017)
- Annual Conference on Neural Information Processing Systems (NeurIPS'2017)
- Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction. [pdf]
- Masamichi Shimosaka, Junichi Sato, Kazuhito Takenaka, Kentarou Hitomi. AAAI 2017.
- Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay. [pdf]
- Haiyan Yin, Sinno Jialin Pan. AAAI 2017.
- OFFER: Off-Environment Reinforcement Learning. [pdf]
- Kamil Andrzej Ciosek, Shimon Whiteson. AAAI 2017.
- Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems. [pdf]
- Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli. AAAI 2017.
- Scalable Multitask Policy Gradient Reinforcement Learning. [pdf]
- Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor. AAAI 2017.
- Dynamic Action Repetition for Deep Reinforcement Learning. [pdf]
- Aravind S. Lakshminarayanan, Sahil Sharma, Balaraman Ravindran. AAAI 2017.
- Playing FPS Games with Deep Reinforcement Learning. [pdf]
- Guillaume Lample, Devendra Singh Chaplot. AAAI 2017.
- Transfer Reinforcement Learning with Shared Dynamics. [pdf]
- Romain Laroche, Merwan Barlier. AAAI 2017.
- Where to Add Actions in Human-in-the-Loop Reinforcement Learning. [pdf]
- Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic. AAAI 2017.
- Automatic Curriculum Graph Generation for Reinforcement Learning Agents. [pdf]
- Maxwell Svetlik, Matteo Leonetti, Jivko Sinapov, Rishi Shah, Nick Walker, Peter Stone. AAAI 2017.
- Self-Correcting Models for Model-Based Reinforcement Learning. [pdf]
- Erik Talvitie. AAAI 2017.
- An Efficient Approach to Model-Based Hierarchical Reinforcement Learning. [pdf]
- Zhuoru Li, Akshay Narayan, Tze-Yun Leong. AAAI 2017.
- Learning Options in Multiobjective Reinforcement Learning. [pdf]
- Rodrigo Cesar Bonini, Felipe Leno da Silva, Anna Helena Reali Costa. AAAI 2017.
- An Advising Framework for Multiagent Reinforcement Learning Systems. [pdf]
- Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa. AAAI 2017.
- Policy Reuse in Deep Reinforcement Learning. [pdf]
- Ruben Glatt, Anna Helena Reali Costa. AAAI 2017.
- Accelerating Multiagent Reinforcement Learning through Transfer Learning. [pdf]
- Felipe Leno da Silva, Anna Helena Reali Costa. AAAI 2017.
- Improving Deep Reinforcement Learning with Knowledge Transfer. [pdf]
- Ruben Glatt, Anna Helena Reali Costa. AAAI 2017.
- Learning Conventions via Social Reinforcement Learning in Complex and Open Settings. [pdf]
- George A. Vouros. AAMAS 2017.
- Multi-agent Reinforcement Learning in Sequential Social Dilemmas. [pdf]
- Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel. AAMAS 2017.
- Scaling Expectation-Maximization for Inverse Reinforcement Learning to Multiple Robots under Occlusion. [pdf]
- Kenneth D. Bogert, Prashant Doshi. AAMAS 2017.
- Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. [pdf]
- Vivek Veeriah, Harm van Seijen, Richard S. Sutton. AAMAS 2017.
- Reward Shaping in Episodic Reinforcement Learning. [pdf]
- Marek Grzes. AAMAS 2017.
- Reinforcement Learning for Multi-Step Expert Advice. [pdf]
- Patrick Philipp, Achim Rettinger. AAMAS 2017.
- Simultaneously Learning and Advising in Multiagent Reinforcement Learning. [pdf]
- Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa. AAMAS 2017.
- Inverse Reinforcement Learning in Swarm Systems. [pdf]
- Adrian Sosic, Wasiur R. KhudaBukhsh, Abdelhak M. Zoubir, Heinz Koeppl. AAMAS 2017.
- Dynamic Generalization Kanerva Coding in Reinforcement Learning for TCP Congestion Control Design. [pdf]
- Wei Li, Fan Zhou, Waleed Meleis, Kaushik R. Chowdhury. AAMAS 2017.
- Autonomous Model Management via Reinforcement Learning: Extended Abstract. [pdf]
- Elad Liebman, Eric Zavesky, Peter Stone. AAMAS 2017.
- Data Driven Strategies for Active Monocular SLAM using Inverse Reinforcement Learning. [pdf]
- Vignesh Prasad, Rishabh Jangir, Balaraman Ravindran, K. Madhava Krishna. AAMAS 2017.
- Analysing Congestion Problems in Multi-agent Reinforcement Learning. [pdf]
- Roxana Radulescu, Peter Vrancx, Ann Nowé. AAMAS 2017.
- Speeding up Tabular Reinforcement Learning Using State-Action Similarities. [pdf]
- Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus. AAMAS 2017.
- Inverse Reinforcement Learning Under Noisy Observations. [pdf]
- Shervin Shahryari, Prashant Doshi. AAMAS 2017.
- Reinforcement Learning with Unsupervised Auxiliary Tasks. [pdf]
- Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu. ICLR 2017.
- Neural Architecture Search with Reinforcement Learning. [pdf]
- Barret Zoph, Quoc V. Le. ICLR 2017.
- Stochastic Neural Networks for Hierarchical Reinforcement Learning. [pdf]
- Carlos Florensa, Yan Duan, Pieter Abbeel. ICLR 2017.
- Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning. [pdf]
- Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine. ICLR 2017.
- Generalizing Skills with Semi-Supervised Reinforcement Learning. [pdf]
- Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine. ICLR 2017.
- Learning to Perform Physics Experiments via Deep Reinforcement Learning. [pdf]
- Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas. ICLR 2017.
- Designing Neural Network Architectures using Reinforcement Learning. [pdf]
- Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar. ICLR 2017.
- Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU. [pdf]
- Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz. ICLR 2017.
- Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning. [pdf]
- Sahil Sharma, Aravind S. Lakshminarayanan, Balaraman Ravindran. ICLR 2017.
- Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening. [pdf]
- Frank S. He, Yang Liu, Alexander G. Schwing, Jian Peng. ICLR 2017.
- Learning to Compose Words into Sentences with Reinforcement Learning. [pdf]
- Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling. ICLR 2017.
- Modular Multitask Reinforcement Learning with Policy Sketches. [pdf]
- Jacob Andreas, Dan Klein, Sergey Levine. ICML 2017.
- Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning. [pdf]
- Oron Anschel, Nir Baram, Nahum Shimkin. ICML 2017.
- An Alternative Softmax Operator for Reinforcement Learning. [pdf]
- Kavosh Asadi, Michael L. Littman. ICML 2017.
- Minimax Regret Bounds for Reinforcement Learning. [pdf]
- Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos. ICML 2017.
- A Distributional Perspective on Reinforcement Learning. [pdf]
- Marc G. Bellemare, Will Dabney, Rémi Munos. ICML 2017.
- Neural Optimizer Search with Reinforcement Learning. [pdf]
- Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le. ICML 2017.
- Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. [pdf]
- Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine. ICML 2017.
- Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution. [pdf]
- Po-Wei Chou, Daniel Maturana, Sebastian A. Scherer. ICML 2017.
- Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. [pdf]
- Jakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson. ICML 2017.
- Reinforcement Learning with Deep Energy-Based Policies. [pdf]
- Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine. ICML 2017.
- DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. [pdf]
- Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner. ICML 2017.
- Fairness in Reinforcement Learning. [pdf]
- Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth. ICML 2017.
- A Laplacian Framework for Option Discovery in Reinforcement Learning. [pdf]
- Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling. ICML 2017.
- Device Placement Optimization with Reinforcement Learning. [pdf]
- Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean. ICML 2017.
- Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. [pdf]
- Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli. ICML 2017.
- Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. [pdf]
- Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian. ICML 2017.
- Why is Posterior Sampling Better than Optimism for Reinforcement Learning? [pdf]
- Ian Osband, Benjamin Van Roy. ICML 2017.
- Robust Adversarial Reinforcement Learning. [pdf]
- Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta. ICML 2017.
- FeUdal Networks for Hierarchical Reinforcement Learning. [pdf]
- Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu. ICML 2017.
- Unifying Task Specification in Reinforcement Learning. [pdf]
- Martha White. ICML 2017.
- Universal Reinforcement Learning Algorithms: Survey and Experiments. [pdf]
- John Aslanides, Jan Leike, Marcus Hutter. IJCAI 2017.
- Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions. [pdf]
- Aijun Bai, Stuart Russell. IJCAI 2017.
- Learning Sparse Representations in Reinforcement Learning with Sparse Coding. [pdf]
- Lei Le, Raksha Kumaraswamy, Martha White. IJCAI 2017.
- Constrained Bayesian Reinforcement Learning via Approximate Linear Programming. [pdf]
- Jongmin Lee, Youngsoo Jang, Pascal Poupart, Kee-Eung Kim. IJCAI 2017.
- Count-Based Exploration in Feature Space for Reinforcement Learning. [pdf]
- Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter. IJCAI 2017.
- Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning. [pdf]
- Sanmit Narvekar, Jivko Sinapov, Peter Stone. IJCAI 2017.
- Improving Reinforcement Learning with Confidence-Based Demonstrations. [pdf]
- Zhaodong Wang, Matthew E. Taylor. IJCAI 2017.
- Multi-Task Deep Reinforcement Learning for Continuous Action Control. [pdf]
- Zhaoyang Yang, Kathryn E. Merrick, Hussein A. Abbass, Lianwen Jin. IJCAI 2017.
- Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. [pdf]
- Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun. IJCAI 2017.
- Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects. [pdf]
- Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus. IJCAI 2017.
- Interactive Narrative Personalization with Deep Reinforcement Learning. [pdf]
- Pengcheng Wang, Jonathan P. Rowe, Wookhee Min, Bradford W. Mott, James C. Lester. IJCAI 2017.
- Reinforcement Learning with a Corrupted Reward Channel. [pdf]
- Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg. IJCAI 2017.
- Curriculum Learning in Reinforcement Learning. [pdf]
- Sanmit Narvekar. IJCAI 2017.
- Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning. [pdf]
- El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer. NeurIPS 2017.
- Safe Model-based Reinforcement Learning with Stability Guarantees. [pdf]
- Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause. NeurIPS 2017.
- Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning. [pdf]
- Liangpeng Zhang, Ke Tang, Xin Yao. NeurIPS 2017.
- Repeated Inverse Reinforcement Learning. [pdf]
- Kareem Amin, Nan Jiang, Satinder Singh. NeurIPS 2017.
- Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. [pdf]
- Rowan McAllister, Carl Edward Rasmussen. NeurIPS 2017.
- Compatible Reward Inverse Reinforcement Learning. [pdf]
- Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli. NeurIPS 2017.
- EX2: Exploration with Exemplar Models for Deep Reinforcement Learning. [pdf]
- Justin Fu, John D. Co-Reyes, Sergey Levine. NeurIPS 2017.
- #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. [pdf]
- Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel. NeurIPS 2017.
- Bridging the Gap Between Value and Policy Based Reinforcement Learning. [pdf]
- Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans. NeurIPS 2017.
- Cold-Start Reinforcement Learning with Softmax Policy Gradient. [pdf]
- Nan Ding, Radu Soricut. NeurIPS 2017.
- Reinforcement Learning under Model Mismatch. [pdf]
- Aurko Roy, Huan Xu, Sebastian Pokutta. NeurIPS 2017.
- Shallow Updates for Deep Reinforcement Learning. [pdf]
- Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor. NeurIPS 2017.
- Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. [pdf]
- Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine. NeurIPS 2017.
- Successor Features for Transfer in Reinforcement Learning. [pdf]
- André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt. NeurIPS 2017.
- A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. [pdf]
- Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel. NeurIPS 2017.
- Deep Reinforcement Learning from Human Preferences. [pdf]
- Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei. NeurIPS 2017.
- Online Reinforcement Learning in Stochastic Games. [pdf]
- Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu. NeurIPS 2017.
- Hybrid Reward Architecture for Reinforcement Learning. [pdf]
- Harm van Seijen, Mehdi Fatemi, Romain Laroche, Joshua Romoff, Tavian Barnes, Jeffrey Tsang. NeurIPS 2017.
- Imagination-Augmented Agents for Deep Reinforcement Learning. [pdf]
- Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, Demis Hassabis, David Silver, Daan Wierstra. NeurIPS 2017.
- Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. [pdf]
- Christoph Dann, Tor Lattimore, Emma Brunskill. NeurIPS 2017.