Method predict

rl2/cartpole/td_lambda.py:57–60

(self, s)

    self.eligibilities = np.zeros_like(self.eligibilities)

  def predict(self, s):
    X = self.feature_transformer.transform([s])
    result = np.stack([m.predict(X) for m in self.models]).T
    return result

  def update(self, s, a, G, gamma, lambda_):
    X = self.feature_transformer.transform([s])