Abstract is missing.
- Invited Talk: UCRL and Autonomous ExplorationPeter Auer. 1 [doi]
- Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement LearningKristian Kersting. 2 [doi]
- Invited Talk: PRISM - Practical RL: Representation, Interaction, Synthesis, and MortalityPeter Stone. 3 [doi]
- Invited Talk: Towards Robust Reinforcement Learning AlgorithmsCsaba Szepesvári. 4 [doi]
- Automatic Discovery of Ranking Formulas for Playing with Multi-armed BanditsFrancis Maes, Louis Wehenkel, Damien Ernst. 5-17 [doi]
- Goal-Directed Online Learning of Predictive ModelsSylvie C. W. Ong, Yuri Grinberg, Joelle Pineau. 18-29 [doi]
- Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy ControlMatthew W. Robards, Peter Sunehag. 30-41 [doi]
- Active Learning of MDP ModelsMauricio Araya-López, Olivier Buffet, Vincent Thomas, François Charpillet. 42-53 [doi]
- Handling Ambiguous Effects in Action LearningBoris Lesner, Bruno Zanuttini. 54-65 [doi]
- Feature Reinforcement Learning in PracticePhuong Minh Nguyen, Peter Sunehag, Marcus Hutter. 66-77 [doi]
- Reinforcement Learning with a Bilinear Q FunctionCharles Elkan. 78-88 [doi]
- ℓ1-Penalized Projected Bellman ResidualMatthieu Geist, Bruno Scherrer. 89-101 [doi]
- Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 PenalizationMatthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos. 102-114 [doi]
- Recursive Least-Squares Learning with Eligibility TracesBruno Scherrer, Matthieu Geist. 115-127 [doi]
- Value Function Approximation through Sparse Bayesian ModelingNikolaos Tziortziotis, Konstantinos Blekas. 128-139 [doi]
- Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation MetricsPablo Samuel Castro, Doina Precup. 140-152 [doi]
- Unified Inter and Intra Options Learning Using Policy Gradient MethodsKfir Y. Levy, Nahum Shimkin. 153-164 [doi]
- Options with ExceptionsMunu Sairamesh, Balaraman Ravindran. 165-176 [doi]
- Robust Bayesian Reinforcement Learning through Tight Lower BoundsChristos Dimitrakakis. 177-188 [doi]
- Optimized Look-ahead Tree Search PoliciesFrancis Maes, Louis Wehenkel, Damien Ernst. 189-200 [doi]
- A Framework for Computing Bounds for the Return of a PolicyCosmin Paduraru, Doina Precup, Joelle Pineau. 201-212 [doi]
- Transferring Evolved Reservoir Features in Reinforcement Learning TasksKyriakos C. Chatzidimitriou, Ioannis Partalas, Pericles A. Mitkas, Ioannis P. Vlahavas. 213-224 [doi]
- Transfer Learning via Multiple Inter-task MappingsAnestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas. 225-236 [doi]
- Multi-Task Reinforcement Learning: Shaping and Feature SelectionMatthijs Snel, Shimon Whiteson. 237-248 [doi]
- Transfer Learning in Multi-Agent Reinforcement Learning DomainsGeorgios Boutsioukis, Ioannis Partalas, Ioannis P. Vlahavas. 249-260 [doi]
- An Extension of a Hierarchical Reinforcement Learning Algorithm for Multiagent SettingsIoannis Lambrou, Vassilis Vassiliades, Chris Christodoulou. 261-272 [doi]
- Bayesian Multitask Inverse Reinforcement LearningChristos Dimitrakakis, Constantin A. Rothkopf. 273-284 [doi]
- Batch, Off-Policy and Model-Free Apprenticeship LearningEdouard Klein, Matthieu Geist, Olivier Pietquin. 285-296 [doi]
- Introduction of Fixed Mode States into Online Profit Sharing and Its Application to Waist Trajectory Generation of Biped RobotSeiya Kuroda, Kazuteru Miyazaki, Hiroaki Kobayashi. 297-308 [doi]
- MapReduce for Parallel Reinforcement LearningYuxi Li, Dale Schuurmans. 309-320 [doi]
- Compound Reinforcement Learning: Theory and an Application to FinanceTohgoroh Matsui, Takashi Goto, Kiyoshi Izumi, Yu Chen. 321-332 [doi]
- Proposal and Evaluation of the Active Course Classification Support System with Exploitation-Oriented LearningKazuteru Miyazaki, Masaaki Ida. 333-344 [doi]