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IEEE Transactions on Computers
Bisimulation through probabilistic testing
Information and Computation
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Generalized prioritized sweeping
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning Bayesian networks with local structure
Learning in graphical models
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Neuro-Dynamic Programming
Structured Prioritised Sweeping
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Efficient Algorithm for Minimizing Real-time Transition Systems
CAV '93 Proceedings of the 5th International Conference on Computer Aided Verification
Symbolic heuristic search for factored Markov decision processes
Eighteenth national conference on Artificial intelligence
Abstraction in Control Learning
Abstraction in Control Learning
Learning to act using real-time dynamic programming
Artificial Intelligence
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A causal approach to hierarchical decomposition of factored MDPs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Causal Graph Based Decomposition of Factored MDPs
The Journal of Machine Learning Research
Weighted A∗ search -- unifying view and application
Artificial Intelligence
Decision-theoretic planning with non-Markovian rewards
Journal of Artificial Intelligence Research
Topological value iteration algorithm for Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Active learning of dynamic Bayesian networks in Markov decision processes
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Symbolic bounded real-time dynamic programming
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Topological value iteration algorithms
Journal of Artificial Intelligence Research
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Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line planners. In addition to reducing computation time, symbolic generalization can reduce the amount of costly real-world interactions required for convergence. We introduce Symbolic Real-Time Dynamic Programming (or sRTDP), an extension of RTDP. After each step of on-line interaction with an environment, sRTDP uses symbolic model-checking techniques to generalizes its experience by updating a group of states rather than a single state. We examine two heuristic approaches to dynamic grouping of states and show that they accelerate the planning process significantly in terms of both CPU time and the number of steps of interaction with the environment.