Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Approximate solutions to markov decision processes
Approximate solutions to markov decision processes
Learning low dimensional predictive representations
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Machine Learning
Learning predictive state representations using non-blind policies
ICML '06 Proceedings of the 23rd international conference on Machine learning
Improving approximate value iteration using memories and predictive state representations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Abstraction in predictive state representations
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Using predictive representations to improve generalization in reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Point-based planning for predictive state representations
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
The Journal of Machine Learning Research
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We present an algorithmic approach for integrated learning and planning in predictive representations. The approach extends earlier work on predictive state representations to the case of online exploration, by allowing exploration of the domain to proceed in a goal-directed fashion and thus be more efficient. Our algorithm interleaves online learning of the models, with estimation of the value function. The framework is applicable to a variety of important learning problems, including scenarios such as apprenticeship learning, model customization, and decision-making in non-stationary domains.