# The MDP2 package

#### Lars Relund lars@relund.dk

#### 2026-06-04

The `MDP2` package in R is a package for solving Markov decision processes (MDPs) with discrete time-steps, states and actions. Both traditional MDPs (Puterman 1994), semi-Markov decision processes (semi-MDPs) (Tijms 2003) and hierarchical-MDPs (HMDPs) (Kristensen and Jørgensen 2000) can be solved under a finite and infinite time-horizon.

Building and solving an MDP is done in two steps. First, the MDP is built and saved in a set of binary files. Next, you load the MDP into memory from the binary files and apply various algorithms to the model.

The package implement well-known algorithms such as policy iteration and value iteration under different criteria e.g. average reward per time unit and expected total discounted reward. The model is stored using an underlying data structure based on the state-expanded directed hypergraph of the MDP (Nielsen and Kristensen (2006)) implemented in `C++` for fast running times.

To illustrate the package capabilities have a look at the vignettes:

- Building MDP models `vignette("building")`.
- Solving an infinite-horizon MDP (semi-MDP) `vignette("infinite-mdp")`.
- Solving a finite-horizon MDP (semi-MDP) `vignette("finite-mdp")`.
- Solving an infinite-horizon HMDP `vignette("infinite-hmdp")`.

## References

Kristensen, A. R., and E. Jørgensen. 2000. “Multi-Level Hierarchic Markov Processes as a Framework for Herd Management Support.” Annals of Operations Research 94: 69–89. https://doi.org/10.1023/A:1018921201113.
Nielsen, L. R., and A. R. Kristensen. 2006. “Finding the \(K\) Best Policies in a Finite-Horizon Markov Decision Process.” European Journal of Operational Research 175 (2): 1164–79. https://doi.org/10.1016/j.ejor.2005.06.011.
Puterman, M. L. 1994. Markov Decision Processes. Wiley Series in Probability and Mathematical Statistics. Wiley-Interscience.
Tijms, Henk. C. 2003. A First Course in Stochastic Models. John Wiley & Sons Ltd.