Deceptive levels are levels where agents are trapped in the game and the policy leads them away from the global optimum policy. In this paper, we examine the problem of evaluating deceptive levels. In our previous work, we generated deceptive levels and evaluated them using different agents. The interesting insight from that study was that there was no single agent being able to solve all types of deceptions. In order to further extend our work, we investigate the effect of different metrics on the evaluation of deceptive levels. In our current work, we use different metrics including reward penalization, discounted reward, noisy world, broken world, and broken forward model. Initially, we execute these agents by utilizing deceptive as well as non-deceptive levels to perform a comparison on the basis of total points and win-rate. After comparison, ranks are assigned to the agents. Contrary to our previous evaluation, these metrics give different results and rankings. The experiment implies that each agent has a different set of characteristics and weaknesses.