# SPUDD: Stochastic Planning using Decision Diagrams

arXiv: https://arxiv.org/abs/1301.6704 · HTML: https://mlsanity.com/paper/1301.6704/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2013.

## Built on by (most cited first, 7 of 7)

- [dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts](https://mlsanity.com/paper/2101.07202/) (2021)
- [Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning](https://mlsanity.com/paper/2406.03234/) (2024)
- [Uniqueness and Complexity of Inverse MDP Models](https://mlsanity.com/paper/2206.01192/) (2022)
- [A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making](https://mlsanity.com/paper/2304.10590/) (2023)
- [Approximate Inference for Stochastic Planning in Factored Spaces](https://mlsanity.com/paper/2203.12139/) (2022)
- [Explaining Control Policies through Predicate Decision Diagrams](https://mlsanity.com/paper/2503.06420/) (2025)
- [Scalable Precise Computation of Shannon Entropy](https://mlsanity.com/paper/2502.01160/) (2025)

## Abstract (arXiv)

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).