POMDPs

Linux	Mac OS X

This package provides a core interface for working with Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). For examples, please see POMDPExamples, QuickPOMDPs, and the Gallery.

Our goal is to provide a common programming vocabulary for:

1. Expressing problems as MDPs and POMDPs.
2. Writing solver software.
3. Running simulations efficiently.

There are nested interfaces for expressing and interacting with (PO)MDPs: When the explicit interface is used, the transition and observation probabilities are explicitly defined using api functions; when the generative interface is used, only a single step simulator (e.g. (s', o, r) = G(s,a)) needs to be defined. Problems may also be defined with probability tables, or with the simplified QuickPOMDPs interfaces.

Python can be used to define and solve MDPs and POMDPs via the QuickPOMDPs or tabular interfaces and pyjulia (Example: tiger.py).

For help, please post to the Google group, or on gitter. Check releases for information on changes. POMDPs.jl and all packages in the JuliaPOMDP project are fully supported on Linux and OS X. Windows is supported for all native solvers*, and most non-native solvers should work, but may require additional configuration.

Installation

To install POMDPs.jl, run the following from the Julia REPL:

Pkg.add("POMDPs")

To install supported JuliaPOMDP packages including various solvers, first run:

using POMDPs
POMDPs.add_registry()

This installs the JuliaPOMDP registry so that the Julia package manager can find all the available solvers and support packages.

To check available JuliaPOMDP packages, run:

using POMDPs
POMDPs.available()

To install a particular solver (say SARSOP.jl), having installed the Registry, run:

Pkg.add("SARSOP")

Quick Start

To run a simple simulation of the classic Tiger POMDP using a policy created by the QMDP solver, you can use the following code (Note that this uses the simplified QuickPOMDPs interface; the main interface has much more expressive power):

using POMDPs, QuickPOMDPs, POMDPSimulators, QMDP

S = [:left, :right]
A = [:left, :right, :listen]
O = [:left, :right]
γ = 0.95

function T(s, a, sp)
    if a == :listen
        return s == sp
    else # a door is opened
        return 0.5 #reset
    end
end

function Z(a, sp, o)
    if a == :listen
        if o == sp
            return 0.85
        else
            return 0.15
        end
    else
        return 0.5
    end
end

function R(s, a)
    if a == :listen  
        return -1.0
    elseif s == a # the tiger was found
        return -100.0
    else # the tiger was escaped
        return 10.0
    end
end

m = DiscreteExplicitPOMDP(S,A,O,T,Z,R,γ)

solver = QMDPSolver()
policy = solve(solver, m)

rsum = 0.0
for (s,b,a,o,r) in stepthrough(m, policy, "s,b,a,o,r", max_steps=10)
    println("s: $s, b: $([pdf(b,s) for s in S]), a: $a, o: $o")
    global rsum += r
end
println("Undiscounted reward was $rsum.")

For more examples with visualization see POMDPGallery.jl.

Tutorials

Several tutorials are hosted in the POMDPExamples repository.

Documentation

Detailed documentation can be found here.

Supported Packages

Many packages use the POMDPs.jl interface, including MDP and POMDP solvers, support tools, and extensions to the POMDPs.jl interface.

Tools:

POMDPs.jl itself contains only the interface for communicating about problem definitions. Most of the functionality for interacting with problems is actually contained in several support tools packages:

Package	Build	Coverage
POMDPModelTools
BeliefUpdaters
POMDPPolicies
POMDPSimulators
POMDPModels
POMDPTesting
ParticleFilters
RLInterface

MDP solvers:

Package	Build/Coverage	Online/ Offline	Continuous States	Continuous Actions
Value Iteration		Offline	N	N
Local Approximation Value Iteration		Offline	Y	N
Monte Carlo Tree Search		Online	Y (DPW)	Y (DPW)

POMDP solvers:

Package	Build/Coverage	Online/ Offline	Continuous States	Continuous Actions	Continuous Observations
QMDP		Offline	N	N	N
FIB		Offline	N	N	N
SARSOP*		Offline	N	N	N
BasicPOMCP		Online	Y	N	N1
ARDESPOT		Online	Y	N	N1
MCVI		Offline	Y	N	Y
POMDPSolve*		Offline	N	N	N
IncrementalPruning		Offline	N	N	N
POMCPOW		Online	Y	Y2	Y
AEMS		Online	N	N	N

¹: Will run, but will not converge to optimal solution

²: Will run, but convergence to optimal solution is not proven, and it will likely not work well on multidimensional action spaces

Reinforcement Learning:

Package	Build/Coverage	Continuous States	Continuous Actions
TabularTDLearning		N	N
DeepQLearning		Y1	N

¹: For POMDPs, it will use the observation instead of the state as input to the policy. See RLInterface.jl for more details.

Packages Awaiting Update

These packages were written for POMDPs.jl in Julia 0.6 and have not been updated to 1.0 yet.

Package	Build	Coverage
DESPOT

Performance Benchmarks:

Package
DESPOT

*These packages require non-Julia dependencies

Citing POMDPs

If POMDPs is useful in your research and you would like to acknowledge it, please cite this paper:

@article{egorov2017pomdps,
  author  = {Maxim Egorov and Zachary N. Sunberg and Edward Balaban and Tim A. Wheeler and Jayesh K. Gupta and Mykel J. Kochenderfer},
  title   = {{POMDP}s.jl: A Framework for Sequential Decision Making under Uncertainty},
  journal = {Journal of Machine Learning Research},
  year    = {2017},
  volume  = {18},
  number  = {26},
  pages   = {1-5},
  url     = {http://jmlr.org/papers/v18/16-300.html}
}