Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Bayesian Framework for Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Forward search value iteration for POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A bayesian reinforcement learning approach for customizing human-robot interfaces
Proceedings of the 14th international conference on Intelligent user interfaces
A POMDP approach to P300-based brain-computer interfaces
Proceedings of the 15th international conference on Intelligent user interfaces
Bayesian reinforcement learning in continuous pomdps with Gaussian processes
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Provably Efficient Learning with Typed Parametric Models
The Journal of Machine Learning Research
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
The Journal of Machine Learning Research
Reinforcement Learning of Communication in a Multi-agent Context
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning form experience: a bayesian network based reinforcement learning approach
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
On the performance evaluation of a vision-based human-robot interaction framework
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
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Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that increase an agent's reward. Unfortunately, most POMDPs are defined with a large number of parameters which are difficult to specify only from domain knowledge. In this paper, we present an approximation approach that allows us to treat the POMDP model parameters as additional hidden state in a "model-uncertainty" POMDP. Coupled with model-directed queries, our planner actively learns good policies. We demonstrate our approach on several POMDP problems.