R package pomdp - Infrastructure for Partially Observable Markov
Decision Processes (POMDP)
R package pomdp - Infrastructure for Partially Observable Markov
Decision Processes (POMDP)
A partially observable Markov decision process (POMDP) models an agent decision process where the agent cannot directly observe the environment’s state, but has to rely on observations. The goal is to find an optimal policy to guide the agent’s actions.
The pomdp package provides the infrastructure to define
and analyze the solutions of optimal control problems formulated as
Partially Observable Markov Decision Processes (POMDP). The package uses
the solvers from pomdp-solve
(Cassandra, 2015) available in the companion R package pomdpSolve
to solve POMDPs using a variety of exact and approximate algorithms.
The package provides fast functions (using C++, sparse matrix
representation, and parallelization with foreach) to
perform experiments (sample from the belief space, simulate
trajectories, belief update, calculate the regret of a policy). The
package also interfaces to the following algorithms:
If you are new to POMDPs then start with the POMDP Tutorial.
To cite package ‘pomdp’ in publications use:
Hahsler M (2023). pomdp: Infrastructure for Partially Observable Markov Decision Processes (POMDP). R package version 1.1.2, https://CRAN.R-project.org/package=pomdp.
@Manual{,
title = {pomdp: Infrastructure for Partially Observable Markov Decision
Processes (POMDP)},
author = {Michael Hahsler},
year = {2023},
note = {R package version 1.1.2},
url = {https://CRAN.R-project.org/package=pomdp},
}Stable CRAN version: Install from within R with
install.packages("pomdp")Current development version: Install from r-universe.
install.packages("pomdp",
repos = c("https://mhahsler.r-universe.dev". "https://cloud.r-project.org/"))Solving the simple infinite-horizon Tiger problem.
library("pomdp")
data("Tiger")
Tiger## POMDP, list - Tiger Problem
## Discount factor: 0.75
## Horizon: Inf epochs
## Size: 2 states / 3 actions / 2 obs.
## Normalized: FALSE
##
## Solved: FALSE
##
## List components: 'name', 'discount', 'horizon', 'states', 'actions',
## 'observations', 'transition_prob', 'observation_prob', 'reward',
## 'start', 'terminal_values'sol <- solve_POMDP(model = Tiger)
sol## POMDP, list - Tiger Problem
## Discount factor: 0.75
## Horizon: Inf epochs
## Size: 2 states / 3 actions / 2 obs.
## Normalized: FALSE
##
## Solved:
## Method: grid
## Solution converged: TRUE
## # of alpha vectors: 5
## Total expected reward: 1.933439
##
## List components: 'name', 'discount', 'horizon', 'states', 'actions',
## 'observations', 'transition_prob', 'observation_prob', 'reward',
## 'start', 'solution'Display the value function.
plot_value_function(sol, ylim = c(0, 20))
Display the policy graph.
plot_policy_graph(sol)
Development of this package was supported in part by National Institute of Standards and Technology (NIST) under grant number 60NANB17D180.