libpomdpis an implementation of different offline and online Partially Observable Markov Decision Process (POMDP) approximation algorithms. The code is a combination of Java, Matlab, and some Jython.
libpomdp has different dependencies, according to what algorithm you want to run:
- the Matlab implementation of [1],
- the Symbolic Perseus Package [5],
- matrix-toolkits-java [9].
libpomdp was started by Diego Maniloff at the University of Illinois at Chicago and is now being jointly developed with Mauricio Araya from INRIA at Nancy. We always welcome POMDP researchers to fork the project and help us out.
Copyright (c) 2009, 2010, 2011 Diego Maniloff.
Copyright (c) 2010, 2011 Mauricio Araya.
- Getting Started
- Implemented algorithms
- Documentation
- References
$ git clone [email protected]:dmaniloff/libpomdp.git
$ cd libpomdp
$ ant dist
On its way.
On its way.
[1] Spaan, M. T.J, and N. Vlassis. "Perseus: Randomized point-based value iteration for POMDPs." Journal of Artificial Intelligence Research 24 (2005): 195-220.
[2] Ross, S., J. Pineau, S. Paquet, and B. Chaib-draa. "Online planning algorithms for POMDPs." Journal of Artificial Intelligence Research 32 (2008): 663-704.
[4] Hansen, Eric A. "Solving POMDPs by Searching in Policy Space" (1998): 211-219.
[5] Poupart, Pascal. "Exploiting structure to efficiently solve large scale partially observable markov decision processes." University of Toronto, 2005.
[6] Milos Hauskrecht, "Value-function approximations for partially observable Markov decision processes." Journal of Artificial Intelligence Research (2000).
[7] T. Smith and R. Simmons, "Heuristic search value iteration for POMDPs." in Proceedings of the 20th conference on Uncertainty in artificial intelligence, 2004, 520-527.
[8] Universal Java Matrix Package, http://www.ujmp.org/
[8] matrix-toolkits-java, http://code.google.com/p/matrix-toolkits-java/