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neat-task.hpp
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/** | |
* Main task idea. | |
*/ | |
class Task | |
{ | |
double Evalaute(Genome& genome) | |
{ | |
// This is just a super simple example that shows how to set the input | |
// values. | |
genome.Input() = arma::vec("1 2 0.3"); | |
// Calculate the fitness using the output. | |
genome.Output(); | |
double loss = ..; | |
return loss | |
} | |
} | |
/** | |
* Task idea that wraps the RL environment. | |
*/ | |
template<class EnvironmentType> | |
class Task | |
{ | |
Task(EnvironmentType& environment) : environment(environment) | |
{ | |
} | |
double Evalaute(Genome& genome) | |
{ | |
// Set the initial state. | |
State state = environment.InitialSample(); | |
genome.Input() = state.Data(); | |
double loss = 0; | |
while (!environment.IsTerminal()) | |
{ | |
// In this case we expect that genome.Output() | |
// returns a vector of length 1, that does represent | |
// the action to take at this state. | |
// In case genome will return a vector of length | |
// number of actions we could search for the maximum | |
// value and return the index of that value in the vector. | |
Action action = static_cast<Action>(genome.Output()[0]); | |
// Use the current action to get the next state. | |
loss += environment.Sample(state, action, state); | |
// Update the state of the genome for the next step. | |
genome.Input() = state.Data(); | |
} | |
return loss; | |
} | |
private: | |
EnvironmentType environment; | |
} |
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