Dean, T., R. Givan, and K. Kim (1998). Solving stochastic planning problems with large state and action spaces. In Proc. Fourth International Conference on Artificial Intelligence Planning Systems. Home/Search Document Details and Download
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Plan Generation And Hard Real-Time Execution With Application To.. - Atkins (1999) (1 citation) (Correct)
....state probabilities like those found in P. Then, algorithms such as Policy Iteration and Value Iteration [11] are used to compute an optimal policy (i.e. mapping of actions, along with deadlines for our model, to states) For large problems, recent advances such as factoring the state space [14] and using algebraic decision diagrams to solve factored MDPs [30] have been developed. We have not yet verified whether these methods will be capable of significantly reducing complexity for our problems of interest. We have devoted this section exclusively to a discussion of MDPs, but have opted ....
T. Dean, R. Givan, and K. Kim, Solving Stochastic Planning Problems with Large State and Action Spaces, Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems, Pittsburgh, Pennsylvania, (1998).
Reinforcement Learning for Factored Markov Decision Processes - Sallans (2002) (Correct)
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Dean, T., R. Givan, and K. Kim (1998). Solving stochastic planning problems with large state and action spaces. In Proc. Fourth International Conference on Artificial Intelligence Planning Systems.
Vector Quantization applied to Reinforcement Learning - Fernandez, Borrajo (1999) (1 citation) (Correct)
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Thomas Dean, Robert Givan, and KeeEung Kim. Solving stochastic planning problems with large state and action spaces. In Manuela Veloso Reid Simmons and Stephen Smith, editors, Proceedings of the Fourth International Conference on Arti cial Intelligence Planning Systems, pages 102-110, Pittsburgh, PA, June 1998. AAAI Press.
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