Here is a list of the poster titles and links to the extended abstracts for posters that were presented at MSRL. 1. A Conditional Value-at-Risk Approach for Uncertain Markov Decision Processes by Yao-Liang Yu, Csaba Szepesvari, Yuxi Li, Dale Schuurmans 2. A Neural Representation of Reward Prediction Error Identified Using an Axiomatic Model by Robb B. Rutledge, Mark Dean, Andrew Caplin, Paul W. Glimcher 3. A Novel Benchmark Methodology and Data Repository for Real-life Reinforcement Learning by Ali Nouri, Michael L. Littman, Lihong Li, Ronald Parr, Christopher Painter-Wakefield, Gavin Taylor 4. A Practical Reinforcement Learning System for Training Robots Through Human Interaction by Adam White 5. A Recurrent Neural Network Acquires Working Memory Properties by Reward-dependent STDP by Cristina Savin, Jochen Triesch 6. Acting With Confidence: Quantifying Policy Uncertainty for Medical Applications by Daniel J. Lizotte, Eric Laber, Susan Murphy 7. An Algorithm for the Associative Reinforcement Learning Problem by Gabor Bartok, Csaba Szepesvari 8. An Empirical Comparison of Techniques for Learning Models of Markov Decision Processes by Todd Hester, Peter Stone 9. Application of New Temporal Difference Learning Methods for Approximate Solution of Large Linear Systems by Hamid R. Maei, Richard S. Sutton, Csaba Szepesvari 10. Applying Reinforcement Learning to Modular Cooperative Robotic Systems by Javier de Lope, Jose Antonio Martin H., Dario Maravall 11. Bayesian Inference for Efficient Learning in Control by Marc Peter Deisenroth, Carl Edward Rasmussen 12. Can Reinforcement Learning provide a Unifying Framework for the Etiology of Dystonia? by David A Peterson, Terrence Sejnowski, Howard Poizner 13. Categorizing Transfer for Reinforcement Learning
16. Dopamine-encoded prediction errors under different reinforcement learning strategies by Jeremy J. Clark, Shelly B. Flagel, Scott B. Evans, Christina A. Akers, Sarah M. Clinton, Ingo Willuhn, Terry E. Robinson, Huda Akil, Paul E.M. Phillips 17. Dynamic Portfolio Management with Transaction Costs by Alberto Suarez, John Moody, Matthew Saffell 18. Encoding Sequences of Spikes in Spiking Neural Networks through Reinforcement Learning by Filip Ponulak, Stefan Rotter 19. Exploiting Training Regimens to Improve Learning by Peng Zang, Charles Isbell, Andrea Thomaz 20. Feature Selection for Value Function Approximation Using Bayesian Model Selection by Tobias Jung, Peter Stone 21. How Perceptual Categories Influence Trial and Error Learning in Humans by John McDonnell, Todd M. Gureckis 22. Human Decision Making Under Uncertainty And The Need To Dissociate Mean-Variance Analysis And Expected Utility Theory by Mathieu d'Acremont, Peter Bossaerts 23. Improving Bayesian Reinforcement Learning Using Transition Abstraction by Shivaram Kalyanakrishnan, Peter Stone 25. Knows What It Knows: A framework for Self-Aware Learning by Lihong Li, Michael L. Littman, Thomas J. Walsh 26. Knowledge Representation for Autonomous Robotics by Anna Koop, Marc G. Bellemare 27. Learning a Successful Emotional Interaction with an Artificial Partner by Isabella Cattinelli, Massimiliano Goldwurm, N. Alberto Borghese 28. Learning Approximate Predictive Representations by Monica Dinculescu 29. Learning in Uncertain Environments: Comparing Human and Macaque Reinforcement Learning Against an Optimal Benchmark and Quantitatively Measuring Reward Prediction Error in BOLD by E. J. DeWitt, M. Dean, P. W. Glimcher 30. Learning State Space Models from Time Series Data by Jordan Frank 31. Linear Value Function Approximation and Linear Models by Ronald Parr, Gavin Taylor, Christopher Painter-Wakefield, Lihong Li, Michael Littman 32. Model-based Bayesian Reinforcement Learning with Adaptive State Aggregation by Cosmin Paduraru, Arthur Guez, Doina Precup, Joelle Pineau 33. Non-disjoint modularity in reinforcement learning through boosted policies by Kurt Driessens 34. Online Learning in Markov Decision Processes with Arbitratily Changing Rewards and Transitions by Jia Yuan Yu, Shie Mannor 35. Online TD(1) Meets Offline Monte Carlo by Yao-Liang Yu, Yongjian Zhang, Csaba Szepesvari 36. Optimal Human Reinforcement Learning in a Hierarchical Decision Task by Ulrik Beierholm, Klaus Wunderlich, Peter Bossaerts, John P O'Doherty 37. Optimal Online Learning Procedures for Model-Free Policy Evaluation by Tshuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe, Shin Ishii 38. PAC-MDP Reinforcement Learning with Bayesian Priors by John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate 39. Parametric Regret in Uncertain Markov Decision Processes by Huan Xu, Shie Mannor 40. Personalized Intelligent Tutoring System Using Reinforcement Learning by Ankit Malpani, B. Ravindran, Hema A. Murthy 41. Probabilistic Discounting for Modeling Behaviors in Iowa Gambling Task by Takaki Makino, Taiki Takahashi, Hirofumi Nishinaka, Hiroki Fukui 42. Regularization in Reinforcement Learning by Amir massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvari, Shie Mannor 43. Reinforcement Learning for Computational Finance by Yuxi Li, Csaba Szepesvari, Dale Schuurmans 44. Reinforcement Learning Rules for Spiking Neurons Can Learn Spatiotemporal Activity Patterns by Nicolas Fremaux, Wulfram Gerstner 45. RL-Glue: From Grid Worlds to Sensor Rich Robots by Brian Tanner, Adam White, Richard Sutton 46. Robot Navigation Based On Ego Perspective Images by Wendelin Böhmer, Steffen Grünewälder, Klaus Obermayer 47. Skill Chaining: Skill Discovery in Continuous Domains by George Konidaris, Andrew Barto 48. Spike-based Reinforcement Learning in Continuous State and Action Space by Eleni Vasilaki, Robert Urbanczik, Walter Senn, Wulfram Gerstner 49. Temporal Difference Learning by Direct Preconditioning by Hengshuai Yao, Shalabh Bhatnagar, Csaba Szepesvari 50. The BRIO Labyrinth Game - A Testbed for Reinforcement Learning and for Studies on Sensorimotor Learning by Jan Hendrik Metzen, Elsa Kirchner, Larbi Abdenebaoui, Frank Kirchner 51. The Critterbot: A Subjective Robotic Project by M. Bellemare, M. Bowling, T. Degris, A. Koop, C. Rayner, M. Sokolsky, R. Sutton, A. White, E. Wiewiora 52. The Impact of Perceptual Aliasing on Human Learning in a Dynamic Decision Making Task by Lisa Zaval, Louis Tur, Todd M. Gureckis 53. The RLAI Robotic Simulator by Marc G. Bellemare 54. The Role of Basal Ganglia in Performing Simple Reaching Movements: A Computational Model by Madgoom Mohamed, V. S. Chakravarthy, Deepak Subramanian, B. Ravindran 55. Timing in Reinforcement Learning Models of Classical Conditioning by Elliot A. Ludvig, Richard S. Sutton, E. James Kehoe. 56. Towards Autonomous Reinforcement Learning for Self-Adaptive Gas Turbine Control by Alexander Hans, Steffen Udluft 57. Unmasking Neurostimulation Policies for the Treatment of Epilepsy by Keith Bush, Joelle Pineau, Massimo Avoli 58. Using Batch RL to Optimize Neurostimulation Strategies by Arthur Guez, Joelle Pineau 59. Value Function Approximation using the Fourier Basis by George Konidaris, Sarah Osentoski 60. Variable-metric Evolution Strategies for Direct Policy Search by Verena Heidrich-Meisner, Christian Igel These accepted posters will not be presented at the symposium: Automatic Subgoals Discovering Method With State Abstraction In Reinforcement Learning by Marek Lapko, Rudolf Jaksa and Peter Sincak Coordinated Learning in Infinite Markov Games by Francisco S. Melo Efficient Reinforcement Learning in Parameterized Models: The Parameter Elimination Approach by Kirill Dyagilev, Shie Mannor, Nahum Shimkin Reinforcement Learning and Affective Response to Consumer Environments by Gordon R. Foxall, Mirella Yani-de-Soriano |
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