function action1 = Agent10(a,b) %a,b
%#codegen

observation1 = [a;b];

% Reinforcement Learning Toolbox
% Generated on: 13-Jan-2020 12:11:54

action1 = localEvaluate(observation1);
end
%% Local Functions
function action1 = localEvaluate(observation1)
persistent policy
if isempty(policy)
	policy = coder.loadDeepLearningNetwork('Agent10.mat','policy'); 
    % NET = coder.loadDeepLearningNetwork(FILENAME,NETWORK_NAME)
    % where Network_name is the name of the C++ class generated from the
    % network. NETWORK_NAME is a descriptive name for the network object saved in the MAT-file, or the
    % function. It must be a char type that is a valid identifier in C++.
    % the resulting policy is a DAG Network
end
action1 = predict(policy,observation1);

end





