# Getting Started

Before writing our own POMDP problems or solvers, let's try out some of the available solvers and problem models available in JuliaPOMDP.

Here is a short piece of code that solves the Tiger POMDP using QMDP, and evaluates the results. Note that you must have the QMDP, POMDPModels, and POMDPToolbox modules installed.

using POMDPs, QMDP, POMDPModels, POMDPSimulators

# initialize problem and solver
pomdp = TigerPOMDP() # from POMDPModels
solver = QMDPSolver() # from QMDP

# compute a policy
policy = solve(solver, pomdp)

#evaluate the policy
belief_updater = updater(policy) # the default QMDP belief updater (discrete Bayesian filter)
init_dist = initialstate_distribution(pomdp) # from POMDPModels
hr = HistoryRecorder(max_steps=100) # from POMDPSimulators
hist = simulate(hr, pomdp, policy, belief_updater, init_dist) # run 100 step simulation
println("reward: \$(discounted_reward(hist))")

The first part of the code loads the desired packages and initializes the problem and the solver. Next, we compute a POMDP policy. Lastly, we evaluate the results.

There are a few things to mention here. First, the TigerPOMDP type implements all the functions required by QMDPSolver to compute a policy. Second, each policy has a default updater (essentially a filter used to update the belief of the POMDP). To learn more about Updaters check out the Concepts section.