def eval_step(self, state):
        ''' Predict the action for evaluation purpose.

        Args:
            state (numpy.array): current state

        Returns:
            action (int): an action id
            info (dict): A dictionary containing information
        '''
        q_values = self.predict(state)
        best_action = np.argmax(q_values)

        info = {}
        info['values'] = {state['raw_legal_actions'][i]: float(q_values[list(state['legal_actions'].keys())[i]]) for i in range(len(state['legal_actions']))}

        return best_action, info
 Predict the action for evaluation purpose.

        Args:
            state (numpy.array): current state

        Returns:
            action (int): an action id
            info (dict): A dictionary containing information
        
rlcard/agents/dqn_agent.py