You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_MDP_game_KL.m
    path of file: ./repos/bspm/thirdparty/spm12/toolbox/DEM
    
    the code of the file until where you have to start completion: function [MDP] = spm_MDP_game_KL(MDP,varargin)
% action selection using active inference (KL formulation)
% FORMAT [MDP] = spm_MDP_game_KL(MDP,[EU])
%
% EU              - optional flag to invoke expected utility only
%
% MDP.T           - process depth (the horizon)
% MDP.N           - number of variational iterations (default 4)
% MDP.S(N,1)      - true initial state
%
% MDP.A(O,N)      - Likelihood of O outcomes given N hidden states
% MDP.B{M}(N,N)   - transition probabilities among hidden states (priors)
% MDP.C(N,1)      - terminal cost probabilities (prior over hidden states)
% MDP.D(N,1)      - initial prior probabilities (prior over hidden states)
%
% MDP.V(T
    
function in place of the transition probabilities under different levels
% of control.
%
% Partially observed Markov decision processes can be modelled by
% specifying a likelihood (as part of a generative model) and absorbing any
% probabilistic mapping between (isomorphic) hidden states and outcomes
% into the transition probabilities G.
%
% See also: spm_MDP, which uses multiple future states and a mean field 
% approximation for control states – but allows for different actions
% at all times (as in control problems).
%
% See also: spm_MDP_game, which generalises this scheme and replaces prior
% beliefs about KL control with minimisation of expected free energy.
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_MDP_game_KL.m 6329 2015-02-05 19:25:52Z karl $

% set up and preliminaries
%==========================================================================

% options and precision defaults
%--------------------------------------------------------------------------
try, PLOT  = MDP.plot;  catch, PLOT  = 0; end
try, alpha = MDP.alpha; catch, alpha = 8; end
try, beta  = MDP.beta;  catch, beta  = 1; end
try, N     = MDP.N;     catch, N     = 4; end


% set up figure if necessary
%--------------------------------------------------------------------------
if PLOT
    if ishandle(PLOT)
        figure(PLOT); clf
        PLOT = 2;
    else
        spm_figure('GetWin','MDP'); clf
    end
end

% generative model and initial states
%--------------------------------------------------------------------------
T     = MDP.T;                     % process depth (the horizon)
Ns    = size(MDP.B{1},1);          % number of hidden states
Nb    = size(MDP.B,1);             % number of time-depend