text: import numpy as np
  from policies import Policy
  
  class EpsilonMaxVariance(Policy):
    def __init__(self, epsilon):
      super().__init__()
      self.epsilon = epsilon
  
    def pi(self, Q, s):
      actions = Q(s)
      action_keys = list(actions.keys())
      if np.random.rand() < self.epsilon:
        # Implement here, Tue style
        return action_keys[np.argmax([std for mean, std in actions.values()])]
      else:
        # Return greedy action
        return action_keys[np.argmax([mean for mean, std in actions.values()])]
  
    def __str__(self):
      return f"epsilon-max-variance-{self.epsilon}"
  