void Learn(Stat s, ActIdx a, Stat next_s, float reward)
{
QActIdx qa = MaxQActIdx(next_s);
q_table[s.Idx(), a.Idx()] +=
alpha * (reward + gamma * qa.q - q_table[s.Idx(), a.Idx()]);
}
public Actions RunStep(States states)
{
next_stat = new Stat(states, isRed);
if (is_started)
{
float reward = Reward(stat, next_stat);
Learn(stat, act_idx, next_stat, reward);
}
is_started = true;
act_idx = Policy(next_stat);
stat = next_stat;
return(act_idx.Actions(next_stat));
}
QActIdx MaxQActIdx(Stat s)
{
int s_idx = s.Idx();
float max_q = float.NegativeInfinity;
ActIdx max_a = new ActIdx(0);
for (int i = 0; i < ActIdx.max_idx; i++)
{
if (q_table[s_idx, i] > max_q)
{
max_q = q_table[s_idx, i];
max_a = new ActIdx(i);
}
}
return(new QActIdx(max_q, max_a));
}
public QActIdx(float q, ActIdx a_idx)
{
this.q = q;
this.a_idx = a_idx;
}
