[UNMAINTAINED - USE ARDESPOT.jl] Package implementing DESPOT POMDP solver for POMDPs.jl
Author JuliaPOMDP
8 Stars
Updated Last
2 Years Ago
Started In
August 2015


Build Status Coverage Status


This repository contains a Julia language implementation of DESPOT POMDP algorithm (, designed to work with the POMDPs.jl API.

A C++ implementation of DESPOT was developed at National University of Singapore and can be found here:

The code has been tested with Julia v0.5.0.


using POMDPs


Data types

The following DESPOT-specific types are likely to be of interest to problem and application developers:

Type Supertype Comments
DESPOTSolver POMDPs.Solver The main solver type
DESPOTPolicy POMDPs.Policy A custom policy type
DESPOTParticle Any A custom particle type used by the solver and the default belief updater
DESPOTBelief Any A custom belief type containing both a particle-based belief and a solver history log
DESPOTConfig Any A set of DESPOT configuration parameters
DESPOTDefaultRNG POMDPs.AbstractRNG The default multi-platform RNG type that can be used to advance the state of the simulation

When defining problem-specific state, action, and observation types, the problem developer needs to make sure that hash() functions and == operators for these types are defined as well, as they are required by the solver. Problem-specific state, action, and observation spaces must be defined as iterable types, either by using existing iterable containers (such as arrays) or by defining start(), next(), and finish() functions for them. For more on this subject, please see POMDPs.jl documentation and Julia documentation on iteration.

Instantiating a DESPOT solver

The following example illustrates instantiation of a DESPOT solver

solver = DESPOTSolver(lb = custom_lb, 	# reference to the optional custom lower bound
					  ub = custom_ub) 	# reference to the optional custom upper bound

Information on how to construct custom upper and lower bound estimators is provided in section Customization. Additional solver parameters (listed below) can either also be passed as keyword arguments during the solver construction or set at a later point (but before a call to POMDPs.solve is made) by accessing solver.config.[parameter].

Parameter Type Default Value Description
search_depth Int64 90 Maximum depth of the search tree
main_seed UInt32 42 The main random seed used to derive other seeds
time_per_move Float64 1 CPU time allowed per move (in sec), -1 for unlimited
n_particles Int64 500 Number of particles used for belief representation
pruning_constant Float64 0.0 Regularization parameter
eta Float64 0.95 eta*width(root) is the target uncertainty to end a trial
sim_len Int64 -1 Number of moves to simulate, -1 for unlimited
approximate_ubound Bool false If true, solver can allow initial lower bound > upper bound
tiny Float64 1e-6 Smallest significant difference between a pair of numbers
rand_max Int64 2^32-1 Largest possible random number
debug UInt8 0 Level of debug output (0-5), 0 - no output, 5 - most output
random_streams RandomStreams Source of random numbers. RandomStreams is designed to reproduce the behavior in the DESPOT paper, MersenneStreamArray is designed to be more widely compatible. See pomdps_compatibility_tests.jl for examples.

Instantiating the default belief updater

A default particle-filtering belief update type, DESPOTBeliefUpdater, is provided in the package.

Parameter Type Default Value Description
seed UInt32 42 Random seed used in belief updates
n_particles Int64 500 Number of particles used for belief representation
particle_weight_threshold Float64 1e-20 Smallest viable particle weight
eff_particle_fraction Float64 0.05 Min. fraction of effective particles to avoid resampling

Note that the solver and the belief updater values for n_particles should be the same (execution will be stopped if they are different). It is also recommended to use the same rand_max value.

Custom belief updaters can be used as well, as long as they are based on the DESPOTBelief particle belief type (see DESPOT.jl). Please see POMDPs.jl documentation for information on defining and using belief updaters.

Solver customization


A DESPOT solver can be customized with user-provided bounds (which can also be problem-specific).

To define bounds, a user should define a custom type (e.g. MyUpperBound) and implement a function with the following signature

DESPOT.bounds{S}(::MyUpperBound, ::POMDP, ::Vector{DESPOTParticle{S}}, ::DESPOTConfig)

that returns a tuple containing the lower bound and upper bound values. Some examples can be found in pomdps_compatibility_tests.jl, upperBoundNonStochastic.jl, and rockSampleParticleLB.jl.

Running DESPOT on test problems

DESPOT.jl should be compatible with test problems in POMDPModels.jl. So far, however, it has been tested only with the included RockSample.

Rock Sample

To run a RockSample problem in REPL, for example, do the following:

main([grid size],[number of rocks])

Running main() without arguments will execute a simple RockSample example with 4 rocks on a grid size of 4.

Example output for RockSample

Upon successful execution, you should see output of the following form:

********** EXECUTION SUMMARY **********
Number of steps = 11
Undiscounted return = 20.00
Discounted return = 12.62
Runtime = 2.45 sec

Tree Visualization

An example of how to visualize the search tree can be found in test_visualization.jl.


Benchmarking results for DESPOT (on RockSample) can be found in As more problems are tested with DESPOT, their benchmarks will be included as well.


Please feel free to file bug reports and I will try to address them as soon as I am able. Feature requests will be considered as well, but only as time allows.