Problem Descriptions

Meeting in a 2x2 grid 2 agent problem used by Bernstein in his IJCAI-05 paper [pdf] using this file format.

DEC-POMDP problem descriptions in Java

Here is a problem data file with descriptions of some of the benchmark problems we have used. The recycling robot problem (introduced in the UAI-07 paper by Amato, Bernstein and Zilberstein) is case 9, the broadcast channel problem (introduced in the IJCAI-05 paper by Bernstein, Hansen and Zilberstein) is case 10, the two agent tiger problem is case 22 (introduced as Tiger-A in Nair et al. IJCAI-03), the 3x3 grid problem is case 63 (from Amato, Dibangoye and Zilberstein ICAPS-09), the stochastic Mars rover problem is case 78 (from Amato and Zilberstein AAMAS-09) and the box pushing problem is case 99 (from Seuken and Zilberstein UAI-07). The discount factor that we have used in each problem is 0.9.

Nonlinear programming (NLP) formulations and problem data files

These are in AMPL and can be used on the NEOS server. See Optimizing Fixed-size Stochastic Controllers for POMDPs and Decentralized POMDPs by Christopher Amato, Daniel S. Bernstein and Shlomo Zilberstein for more details.


Moore NLP formulation for POMDPs - download
Moore NLP formulation for DEC-POMDPs - download
General Mealy NLP formulation for POMDPs (note that this is the full model without removing variables and constrainsts based on exploiting problem structure) - download
General Mealy NLP formulation for DEC-POMDPs (note that this is the full model without removing variables and constrainsts based on exploiting problem structure) - download

Problem Data

Note that these files are without initialization. Typically, we have used random deterministic controllers which are then optimized. This can be set in the problem data file. Also, the number of nodes can be changed on the first line of the file.
POMDP problems
DEC-POMDP problems

Highest known values

The table below provides the highest known values for  a range of benchmark problems with the common discount factor of 0.9 (except Wireless Network which used 0.99). We also list the paper that each result first appeared.  Note that the results are often an average over a number of runs, so single runs may have higher values than those listed here. Optimal values for finite horizon problems can be found here.

Highest known value Source
Highest known value Source
Highest known value Source
6.9958 MI-NIPS-13
Grid 3x3
Highest known value Source
5.802 DBC-ECML-14
Cooperative Box Pushing
Highest known value Source
224.43 DBC-ECML-14
Recycling Robots
Highest known value Source
31.9291 MI-NIPS-13
Mars Rovers
Highest known value Source
26.94 DBC-ECML-14
Wireless Network
Highest known value Source
-140.37 DBC-ECML-14

To report errors, mistakes, additions, omissions, please send an email to Chris Amato.

PP-NIPS-11 (Pajarinen & Peltonen, NIPS 2011)
MI-NIPS-13 (MacDermed & Isbell, NIPS 2013)
DBC-ECML-14 (Dibangoye, Buffet & Charpillet, ECML 2014)


Mark Gruman has developed several tools for the creation and manipulation of multiagent domains. The format is based on the input files found on Tony's POMDP Page .

tool descriptions.txt

Create an input file by filling in the proper information: (multiagent version)

Use a graphical interface to define a meeting on a grid problem with obstacles: