Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Observable Operator Models for Discrete Stochastic Time Series
Neural Computation
A planning algorithm for predictive state representations
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning predictive representations from a history
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning predictive state representations in dynamical systems without reset
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning predictive state representations using non-blind policies
ICML '06 Proceedings of the 23rd international conference on Machine learning
On-line discovery of temporal-difference networks
Proceedings of the 25th international conference on Machine learning
Proto-predictive representation of states with simple recurrent temporal-difference networks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Combining memory and landmarks with predictive state representations
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Temporal-difference networks with history
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning subjective representations for planning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Closing the learning-planning loop with predictive state representations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Closing the learning-planning loop with predictive state representations
International Journal of Robotics Research
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
PAC-Learning of markov models with hidden state
ECML'06 Proceedings of the 17th European conference on Machine Learning
Goal-Directed online learning of predictive models
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
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Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dynamical system (Littman et al., 2001). We present a learning algorithm that learns a PSR from observational data. Our algorithm produces a variant of PSRs called transformed predictive state representations (TPSRs). We provide an efficient principal-components-based algorithm for learning a TPSR, and show that TPSRs can perform well in comparison to Hidden Markov Models learned with Baum-Welch in a real world robot tracking task for low dimensional representations and long prediction horizons.