private int GetClosestStateIndex(List<InvertedPendulumState> states, InvertedPendulumState curstate)
{
double mindist = float.MaxValue;
int closeststate = 0;
for (int i = 0; i < states.Count; ++i)
{
InvertedPendulumState state = states[i];
double dist = (state.a - curstate.a) * (state.a - curstate.a) + (state.w - curstate.w) * (state.w - curstate.w);
if (mindist > dist)
{
mindist = dist;
closeststate = i;
}
}
return closeststate;
}
public static void GetStateAndAction(int i, int j, int k, out InvertedPendulumState state, out InvertedPendulumAction action)
{
GetState(j, k, out state);
GetAction(i, out action);
}
public static void GetStateIndices(InvertedPendulumState state, out int j, out int k)
{
j = ((int)((state.a / Math.PI + 1) * LEN / 2) + LEN * 1000) % LEN;
k = (int)((state.w / 3 + 1) * LEN / 2); if (k > LEN - 1) k = LEN - 1; if (k < 0) k = 0;
}
public float[, ,] value = new float[3, LEN, LEN];//-1,0,1;-pi..pi;-10..10
public static void GetIndices(InvertedPendulumState state, InvertedPendulumAction action, out int i, out int j, out int k)
{
GetStateIndices(state, out j, out k);
GetActionIndices(action, out i);
}
public static void GetState(int j, int k, out InvertedPendulumState state)
{
double a = (j / (float)(LEN / 2) - 1) * Math.PI;
double w = (k / (float)(LEN / 2) - 1) * 3;
state = new InvertedPendulumState(a, w);
}
public void ReplaceValue(InvertedPendulumState state, InvertedPendulumAction2 action, float value)
{
//TODO! delta??*
igmn.Train(new Vector(new double[] { state.a, state.w, action.action, value }));
}
internal void SetState(InvertedPendulumState state)
{
this.a = state.a;
this.w = state.w;
}
public ReinforcementLearningState State()
{
InvertedPendulumState state = new InvertedPendulumState(a, w);
return state;
}
private void button5_Click(object sender, EventArgs e)
{
adp.Train();
customControl21.Invalidate();
Random r = new Random();
InvertedPendulumQStore QStore = (InvertedPendulumQStore)adp.qstore;
for (int aa = 0; aa < 100000; ++aa)
{
InvertedPendulumState state = new InvertedPendulumState((float)(2 * r.NextDouble() - 1) * Math.PI, (2 * r.NextDouble() - 1) * 3);
InvertedPendulumAction action = new InvertedPendulumAction(r.Next(3) - 1);
((InvertedPendulumESIGMNQStore)esigmn.qFunction).igmn.Train(new Vector(new double[] { state.a, state.w, action.action, QStore.Evaluate(state, action) }));
}
//for (int j = 0; j < QStore.value.GetLength(1); ++j)
//{
// for (int k = 0; k < QStore.value.GetLength(2); ++k)
// {
// for (int i = 0; i < QStore.value.GetLength(0); ++i)
// {
// InvertedPendulumState state;
// InvertedPendulumAction action;
// InvertedPendulumQStore.GetStateAndAction(i, j, k, out state, out action);
// ((InvertedPendulumESIGMNQStore)esigmn.qFunction).igmn.Train(new Vector(new double[] { state.a, state.w, action.action, QStore.value[i, j, k] }));
// }
// }
//}
esigmnViewer1.Invalidate();
}
public void GenerateMMatrices()
{
InvertedPendulumEnvironment testws = new InvertedPendulumEnvironment();
int statenum = InvertedPendulumQStore.LEN * InvertedPendulumQStore.LEN;
Ma = new SparseMatrix[3];
for (int i = 0; i < 3; ++i)
{
Ma[i] = new SparseMatrix(statenum);
}
for (int ii = 0; ii < statenum; ++ii)
{
int j = ii % InvertedPendulumQStore.LEN;
int k = ii / InvertedPendulumQStore.LEN;
InvertedPendulumState state;
InvertedPendulumQStore.GetState(j, k, out state);
for (int i = 0; i < 3; ++i)
{
int action = i - 1;
for (int i1 = 0; i1 < 1000; ++i1)
{
testws.a = state.a + r.NextDouble() / InvertedPendulumQStore.LEN * 2 * Math.PI;
testws.w = state.w + r.NextDouble() / InvertedPendulumQStore.LEN * 2 * 3;
testws.Step(action);
int j2, k2;
InvertedPendulumState state2 = new InvertedPendulumState(testws.a, testws.w);
InvertedPendulumQStore.GetStateIndices(state2, out j2, out k2);
int ii2 = k2 * InvertedPendulumQStore.LEN + j2;
Ma[i][ii, ii2] += 0.001f;
}
}
}
SaveMMatrices("mmatrices.dat");
}