input: Write a python function to calculate the Q-table update for a reinforcement learning agent.
  assert calculate_q_update(0.1, 0.9, 0.5, 0.2, 1) == 0.287
  assert calculate_q_update(0.5, 0.8, 0.6, 0.1, 0) == 0.28
  assert calculate_q_update(0.3, 0.7, 0.4, 0.3, 2) == 0.365
output: def calculate_q_update(alpha, gamma, current_q, reward, max_future_q):
      return current_q + alpha * (reward + gamma * max_future_q - current_q)