original_docstring: 
          Decides on the next action, as selected by an epsilon-Greedy exploration
          policy.
  
          Parameters
          ----------
          state_idx : tuple 
              Tuple describing the current state
          q_func : np.ndarray 
              Current Q-table
          epsilon : float
              Probability of choosing a random action
  
          Returns:
          act_idx: int describing the index of the action to take
          
docstring: Decides on the next action, as selected by an epsilon-Greedy exploration
  policy.
  
  Parameters
  
  state_idx : tuple
  Tuple describing the current state
  q_func : np.ndarray
  Current Q-table
  epsilon : float
  Probability of choosing a random action
docstring_tokens: Decides on the next action as selected by an epsilon - Greedy exploration policy . Parameters state_idx : tuple Tuple describing the current state q_func : np . ndarray Current Q - table epsilon : float Probability of choosing a random action
code: def _epsilon_greedy_strategy(
          self,
          state_idx, 
          q_func, 
          epsilon
      ):
          """
          Decides on the next action, as selected by an epsilon-Greedy exploration
          policy.
  
          Parameters
          ----------
          state_idx : tuple 
              Tuple describing the current state
          q_func : np.ndarray 
              Current Q-table
          epsilon : float
              Probability of choosing a random action
  
          Returns:
          act_idx: int describing the index of the action to take
          """
          randomness = np.random.uniform(0,1)
          
          if randomness < epsilon:
              # Exploration
              act_idx = np.random.choice([0,1])
              
          else:
              # Exploitation
              current_state_q = q_func[state_idx]
              act_idx = np.argmax(current_state_q)
  
          return act_idx
code_tokens: def _epsilon_greedy_strategy ( self , state_idx , q_func , epsilon ) : randomness = np . random . uniform ( 0 , 1 ) if randomness < epsilon : act_idx = np . random . choice ( [ 0 , 1 ] ) else : current_state_q = q_func [ state_idx ] act_idx = np . argmax ( current_state_q ) return act_idx
short_docstring: Decides on the next action, as selected by an epsilon-Greedy exploration
  policy.
short_docstring_tokens: Decides on the next action as selected by an epsilon - Greedy exploration policy .
comment: """
          Decides on the next action, as selected by an epsilon-Greedy exploration
          policy.
  
          Parameters
          ----------
          state_idx : tuple 
              Tuple describing the current state
          q_func : np.ndarray 
              Current Q-table
          epsilon : float
              Probability of choosing a random action
  
          Returns:
          act_idx: int describing the index of the action to take
          """$SEP$# Exploration$SEP$# Exploitation