re-ordered instructions

README.md

@@ -4,7 +4,7 @@
 [![Coverage Status](https://coveralls.io/repos/github/JuliaPOMDP/DiscreteValueIteration.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaPOMDP/DiscreteValueIteration.jl?branch=master)
 
 This package implements the discrete value iteration algorithm in Julia for solving Markov decision processes (MDPs).
-The user should define the problem according to the API in [POMDPs.jl](https://github.com/JuliaPOMDP/POMDPs.jl). Examples of
+The user should define the problem with [QuickPOMDPs.jl](https://github.com/JuliaPOMDP/QuickPOMDPs.jl) or according to the API in [POMDPs.jl](https://github.com/JuliaPOMDP/POMDPs.jl). Examples of
 problem definitions can be found in [POMDPModels.jl](https://github.com/JuliaPOMDP/POMDPModels.jl). For an extensive tutorial, see [these](https://github.com/JuliaPOMDP/POMDPExamples.jl) notebooks.
 
 There are two solvers in the package. The "vanilla" [`ValueIterationSolver`](src/vanilla.jl) calls functions from the POMDPs.jl interface in every iteration, while the [`SparseValueIterationSolver`](src/sparse.jl) first creates sparse transition and reward matrices and then performs value iteration with the new matrix representation. While both solvers take advantage of sparsity, the `SparseValueIterationSolver` is generally faster because of low-level optimizations, while the `ValueIterationSolver` has the advantage that it does not require allocation of transition matrices (which could potentially be too large to fit in memory).
@@ -26,7 +26,28 @@ using Pkg; Pkg.add("DiscreteValueIteration")
 
 ## Usage
 
-Use
+Given an MDP `mdp` defined with [QuickPOMDPs.jl](https://github.com/JuliaPOMDP/QuickPOMDPs.jl) or the POMDPs.jl interface, use 
+
+```julia
+using DiscreteValueIteration
+
+solver = ValueIterationSolver(max_iterations=100, belres=1e-6) # creates the solver
+solve(solver, mdp) # runs value iterations
+```
+To extract the policy for a given state, simply call the action function:
+
+```julia
+a = action(policy, s) # returns the optimal action for state s
+```
+
+Or to extract the value, use
+```julia
+value(policy, s) # returns the optimal value at state s
+```
+
+### Requirements for problems defined using the POMDPs.jl interface
+
+If you are using the POMDPs.jl interface instead of QuickPOMDPs.jl, you can see the requirements for using these solvers with
 
 ```julia
 using POMDPs
@@ -35,7 +56,7 @@ using DiscreteValueIteration
 @requirements_info SparseValueIterationSolver() YourMDP()
 ```
 
-to get a list of POMDPs.jl functions necessary to use the solver. This should return a list of the following functions to be implemented for your MDP:
+This should return a list of the following functions to be implemented for your MDP:
 
 ```julia
 discount(::MDP)
@@ -51,19 +72,3 @@ pdf(::StateDistribution, ::State)
 states(::MDP)
 actions(::MDP)
 ```
-
-Once the above functions are defined, the solver can be called with the following syntax:
-
-```julia
-using DiscreteValueIteration
-
-mdp = MyMDP() # initializes the MDP
-solver = ValueIterationSolver(max_iterations=100, belres=1e-6) # creates the solver
-solve(solver, mdp) # runs value iterations
-```
-
-To extract the policy for a given state, simply call the action function:
-
-```julia
-a = action(policy, s) # returns the optimal action for state s
-```