Markov Decision Processes (MDPs) in R

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.

Installation

Install the latest stable release from CRAN:

install.packages("MDP2")

Alternatively, install the latest development version from GitHub (recommended):

remotes::install_github("relund/mdp")

We load the package using

library(MDP2)

Help about the package can be seen by writing

?MDP2

To illustrate the package capabilities, we use a few examples, namely, an infinite and finite-horizon semi-MDP and a HMDP. Before each example a short introduction to these models are given.

Learning more

To get started, first read vignette("MDP2").

For more examples see example("MDP2").

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.