//-------------------------------Update()--------------------------------
//
// First we take sensor readings and feed these into the sweepers brain.
//
// The inputs are:
//
// The readings from the minesweeper's sensors
// The readings from the minesweeper's feelers
// bCollided
//
// We receive two outputs from the brain.. lTrack & rTrack.
// So given a force for each track we calculate the resultant rotation
// and acceleration and apply to current velocity vector.
//
//-----------------------------------------------------------------------
bool CMinesweeper::Update(vector<SPoint> &objects)
{
//this will store all the inputs for the NN
vector<double> inputs;
//grab sensor readings
TestSensors(objects);
//input sensors into net
for (int sr=0; sr<m_vecdSensors.size(); ++sr)
{
inputs.push_back(m_vecdSensors[sr]);
inputs.push_back(m_vecFeelers[sr]);
}
inputs.push_back(m_bCollided);
//update the brain and get feedback
vector<double> output = m_ItsBrain.Update(inputs);
//make sure there were no errors in calculating the
//output
if (output.size() < CParams::iNumOutputs)
{
return false;
}
//assign the outputs to the sweepers left & right tracks
m_lTrack = output[0];
m_rTrack = output[1];
//calculate steering forces
double RotForce = m_lTrack - m_rTrack;
//clamp rotation
Clamp(RotForce, -CParams::dMaxTurnRate, CParams::dMaxTurnRate);
m_dRotation += RotForce;
//update Look At
m_vLookAt.x = -sin(m_dRotation);
m_vLookAt.y = cos(m_dRotation);
//if the sweepers haven't collided with an obstacle
//update their position
if (!m_bCollided)
{
m_dSpeed = (m_lTrack + m_rTrack);
//update position
m_vPosition += (m_vLookAt * m_dSpeed);
}
//update the memory map
m_MemoryMap.Update(m_vPosition.x, m_vPosition.y);
return true;
}
//-------------------------------Update()--------------------------------
//
// First we take sensor readings and feed these into the sweepers brain.
//
// The inputs are:
//
// The readings from the minesweepers sensors
//
// We receive two outputs from the brain.. lTrack & rTrack.
// So given a force for each track we calculate the resultant rotation
// and acceleration and apply to current velocity vector.
//
//-----------------------------------------------------------------------
bool CMinesweeper::Update(vector<SPoint> &objects)
{
if (m_bActive) {
//this will store all the inputs for the NN
vector<double> inputs;
//grab sensor readings
TestSensors(objects);
//input sensors into net
for (int sr=0; sr<m_vecdSensors.size(); ++sr)
{
inputs.push_back(m_vecdSensors[sr]);
// inputs.push_back(m_vecFeelers[sr]); // No need for feelers
}
// inputs.push_back(m_bCollided);
double reward = m_MemoryMap.CheckReward(m_vPosition.x, m_vPosition.y, m_bReverse);
if (reward > 0.9) {
reward = 0.1;
}
else if (reward > 0) {
reward = 1;
}
inputs.push_back(reward);
double turningPoint = m_MemoryMap.CheckTurningPoint(m_vPosition.x, m_vPosition.y);
inputs.push_back(turningPoint);
vector<double> output;
if (reward < 0.01) {
//update the brain and get feedback
output = m_pItsBrain->Update(inputs, CNeuralNet::active);
} else {
output = m_pItsBrain->Update(inputs, CNeuralNet::snapshot);
}
//make sure there were no errors in calculating the
//output
if (output.size() < CParams::iNumOutputs)
{
return false;
}
//assign the outputs to the sweepers left & right tracks
// m_lTrack = output[0] * 2 - 1;
//m_rTrack = output[1];
m_lTrack = output[0] * 2 - 1;
//calculate steering forces
//double RotForce = m_lTrack - m_rTrack;
//clamp rotation
//Clamp(RotForce, -CParams::dMaxTurnRate, CParams::dMaxTurnRate);
//if(m_lTrack < -0.3) m_dRotation = 0;//3.14159265358979;//3.14159265358979f * (3.0 / 2.0);
//else if(m_lTrack > 0.3) m_dRotation = 3.1415926358979f * 0.5;
//else m_dRotation = 3.1415926358979f * 1.5;
m_dRotation += m_lTrack;
//update Look At
m_vLookAt.x = -sin(m_dRotation);
m_vLookAt.y = cos(m_dRotation);
//if the sweepers haven't collided with an obstacle
//update their position
if (!m_bCollided)
{
m_dSpeed = 3;// + m_rTrack;
//m_dSpeed *= 2;
//update position
m_vPosition += (m_vLookAt * m_dSpeed);
//test range of x,y values - because of 'cheap' collision detection
//this can go into error when using < 4 sensors
TestRange();
}
//update the memory map
m_MemoryMap.Update(m_vPosition.x, m_vPosition.y);
m_bActive = reward < 0.05;
return true;
}
return true;
}
