private void EndEpisode(bool win, double reward)
{
if (validationRun_)
{
tracker_.SaveValidationEpisode(reward, win, steps_);
}
else if (learning)
{
tracker_.SaveEpisode(reward, win, steps_, epsilon_, alpha_);
}
epsilon_ = endEpsilon + (startEpsilon - endEpsilon) * Mathf.Pow(1f - epsilonDecay, episodes_);
alpha_ = endLearningRate + (startLearningRate - endLearningRate) * Mathf.Pow(1f - learningDecay, episodes_);
episodes_++;
lastReward_ = reward;
if (reward_ > bestReward_)
{
bestReward_ = reward;
}
sumReward_ += reward;
if (win)
{
wins_++;
}
else
{
fails_++;
}
savedRewards_[saveInd_] = reward;
savedWins_[saveInd_] = win;
saveInd_ = (saveInd_ + 1) % nSaves_;
// Learning stop logic
if (!stopped && episodes_ >= stopAfter)
{
tracker_.EndRun();
superSpeed = 1;
stopped = true;
learning = false;
}
else if (!stopped)
{
validationRun_ = episodes_ % validateEvery == 0;
}
else
{
validationRun_ = false;
}
Reset();
}
