original_docstring: 
          React to a state by returning the most probable action according to an
          epsilon-greedy policy.
           
          Args:
              state:
                  The current state 
              eps:
                  Epsilon value in the epsilon-greedy policy
          
docstring: React to a state by returning the most probable action according to an
  epsilon-greedy policy.
docstring_tokens: React to a state by returning the most probable action according to an epsilon - greedy policy .
code: def react(self,state,eps):
          """
          React to a state by returning the most probable action according to an
          epsilon-greedy policy.
           
          Args:
              state:
                  The current state 
              eps:
                  Epsilon value in the epsilon-greedy policy
          """
          
          state = torch.from_numpy(state).float().unsqueeze(0).to(device)
          self.dqnLocal.eval()
          with torch.no_grad():
              actions = self.dqnLocal(state)
          self.dqnLocal.train()
  
          # Epsilon-greedy action selection
          if random.random() > eps:
              return np.argmax(actions.cpu().data.numpy())
          else:
              return random.choice(np.arange(self.actionSize))
code_tokens: def react ( self , state , eps ) : state = torch . from_numpy ( state ) . float ( ) . unsqueeze ( 0 ) . to ( device ) self . dqnLocal . eval ( ) with torch . no_grad ( ) : actions = self . dqnLocal ( state ) self . dqnLocal . train ( ) if random . random ( ) > eps : return np . argmax ( actions . cpu ( ) . data . numpy ( ) ) else : return random . choice ( np . arange ( self . actionSize ) )
short_docstring: React to a state by returning the most probable action according to an
  epsilon-greedy policy.
short_docstring_tokens: React to a state by returning the most probable action according to an epsilon - greedy policy .
comment: """
          React to a state by returning the most probable action according to an
          epsilon-greedy policy.
           
          Args:
              state:
                  The current state 
              eps:
                  Epsilon value in the epsilon-greedy policy
          """$SEP$# Epsilon-greedy action selection