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QMDP

This Julia package implements the QMDP approximate solver for POMDP/MDP planning. The QMDP solver is documented in:

• Michael Littman, Anthony Cassandra, and Leslie Kaelbling. "Learning policies for partially observable environments: Scaling up." In Proceedings of the Twelfth International Conference on Machine Learning, pages 362--370, San Francisco, CA, 1995.

Installation

import Pkg
Pkg.add("QMDP")

Usage

using QMDP
pomdp = MyPOMDP() # initialize POMDP

# initialize the solver
# key-word args are the maximum number of iterations the solver will run for, and the Bellman tolerance
solver = QMDPSolver(max_iterations=20,
                    belres=1e-3,
                    verbose=true
                   ) 

# run the solver
policy = solve(solver, pomdp)

To compute optimal action, define a belief with the distribution interface, or use the DiscreteBelief provided in POMDPTools.

using POMDPTools
b = uniform_belief(pomdp) # initialize to a uniform belief
a = action(policy, b)

In order to use the efficient SparseValueIterationSolver from DiscreteValueIteration.jl, you can directly pass the solver to the QMDPSolver constructor as follows:

using QMDP, DiscreteValueIteration
pomdp = MyPOMDP()

solver = QMDPSolver(SparseValueIterationSolver(max_iterations=20, verbose=true))

policy = solve(solver, pomdp)