SPUDD: Stochastic Planning using Decision Diagrams
arXiv:1301.6704
Abstract
Markov decisions processes (MDPs) are becoming increasing popular as models of decision theoretic planning. While traditional dynamic programming methods perform well for problems with small state spaces, structured methods are needed for large problems. We propose and examine a value iteration algorithm for MDPs that uses algebraic decision diagrams(ADDs) to represent value functions and policies. An MDP is represented using Bayesian networks and ADDs and dynamic programming is applied directly to these ADDs. We demonstrate our method on large MDPs (up to 63 million states) and show that significant gains can be had when compared to tree-structured representations (with up to a thirty-fold reduction in the number of nodes required to represent optimal value functions).
Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)
References in corpus (5)
- Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
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Cited by in corpus (11)
- Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
- Efficient Solution Algorithms for Factored MDPs
- Symbolic Generalization for On-line Planning
- Symbolic Dynamic Programming for Discrete and Continuous State MDPs
- Value-Directed Belief State Approximation for POMDPs
- Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards
- Chi-square Tests Driven Method for Learning the Structure of Factored MDPs
- Approximate Linear Programming for First-order MDPs
- Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
- Counterexample-guided Planning
- Dynamic Programming for Structured Continuous Markov Decision Problems