Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra
Maintainer: Michael Hahsler
Provides pomdp-solve (Cassandra, 2015) to solve POMDPs using a variety of algorithms. A convenient interface is provided in package pomdp.
To cite package ‘pomdpSolve’ in publications use:
Hahsler M, Cassandra AR (2025). “Pomdp: A computational infrastructure for partially observable Markov decision processes.” The R Journal, 16(2), 1-18. ISSN 2073-4859. doi:10.32614/RJ-2024-021 https://doi.org/10.32614/RJ-2024-021.
@Article{,
title = {Pomdp: A computational infrastructure for partially observable Markov decision processes},
author = {Michael Hahsler and Anthony R. Cassandra},
year = {2025},
journal = {The R Journal},
volume = {16},
number = {2},
pages = {1--18},
doi = {10.32614/RJ-2024-021},
issn = {2073-4859},
}
Stable CRAN version: Install from within R with
install.packages("pomdpSolve")
Current development version: Install from r-universe.
install.packages("pomdpSolve",
repos = c("https://mhahsler.r-universe.dev",
"https://cloud.r-project.org/"))
Solve a POMDP file that ships with this package in a temporary directory and read the solution.
library(pomdpSolve)
old_wd <- setwd(tempdir())
file.copy(system.file("tiger.aaai.POMDP", package = "pomdpSolve"), "./tiger.aaai.POMDP")
## [1] TRUE
pomdp_solve("tiger.aaai.POMDP", options = list(method = "grid"), verbose = FALSE)
dir()
## [1] "tiger.aaai-0.alpha" "tiger.aaai-0.pg" "tiger.aaai.POMDP"
read_alpha_file("tiger.aaai-0.alpha")
## V1 V2
## [1,] -98.5 11.5
## [2,] -10.9 6.5
## [3,] 1.9 1.9
## [4,] 6.5 -10.9
## [5,] 11.5 -98.5
read_pg_file("tiger.aaai-0.pg")
## N A Z1 Z2
## 1 1 2 3 3
## 2 2 1 3 1
## 3 3 1 4 2
## 4 4 1 5 3
## 5 5 3 3 3
setwd(old_wd)
The value function is defined by a set of alpha vectors represented as rows in a matrix with $V$ components. Each alpha vector corresponds to a node with the same row number in the policy graph. The policy graph represents the policy in the form of a data.frame with one row for each node. Nodes have a node id $N$, the action $A$ defined by the policy and then for each observation $Z_i$ the id (row) of the policy graph node to transition to.
A detailed description of the available solver options can be found here.