| MDP Toolbox for MATLAB |  |
| MDP Toolbox for MATLAB | |
mdp_LP
Solves discounted MDP with linear programming.
Syntax
[V, policy, cpu_time] = mdp_LP(P, R, discount)
Description
mdp_LP applies linear programming to solve discounted MDP.
The algorithm uses the linprog function of the MATLAB Optimization Toolbox.
No additional display in verbose mode.
Arguments
P can be a 3 dimensions array (SxSxA) or a cell array (1xA), each cell containing a sparse matrix (SxS).
R can be a 3 dimensions array (SxSxA) or a cell array (1xA), each cell containing a sparse matrix (SxS) or a 2D array (SxA) possibly sparse.
discount is a real which belongs to ]0; 1[
Evaluations
V is a (Sx1) vector.
policy is a (Sx1) vector. Each element is an integer
corresponding to an action which maximizes the value function.
Example
>> P(:,:,1) = [ 0.5 0.5; 0.8 0.2 ];
>> P(:,:,2) = [ 0 1; 0.1 0.9 ];
>> R = [ 5 10; -1 2 ];
>> [V, policy,cpu_time] = mdp_LP(P, R, 0.9)
Optimization terminated successfully.
V =
42.4419
36.0465
policy =
2
1
cpu_time =
0.3600
In the above example, P can be a cell array containing sparse matrices:
>> P{1} = sparse([ 0.5 0.5; 0.8 0.2 ]);
>> P{2} = sparse([ 0 1; 0.1 0.9 ]);
The function call is unchanged.
| MDP Toolbox for MATLAB |
|