RL utility function exponentially_weighted_average for Calculating sum of discounted future reward or TD(lambda)

exponentially_weighted_average for Reinforcement Learning.py

import numpy as np

def exponentially_weighted_matrix(discount, mat_len):
    DisMat = np.triu(np.ones((mat_len, mat_len)) * discount, k=1)
    DisMat[DisMat==0] = 1
    DisMat = np.cumprod(DisMat, axis=1)
    DisMat = np.triu(DisMat)
    return DisMat

def exponentially_weighted_cumsum(discount, np_data):
    DisMat = exponentially_weighted_matrix(discount, np_data.shape[0])
    value = np.dot(DisMat, np_data.reshape(-1, 1))
    return value[::-1].transpose()[0]