In prior work, discrete-weight neural networks trained via evolutionary algorithms have been investigated, demonstrating the feasibility of binary-weight models on reinforcement learning tasks including Atari Space Invaders. In this study, we extend this line of research by evaluating ternary-weight neural networks with weights in {-1,0,1} and comparing their performance with binary-weight counterparts {-1,1}. Using Evolution Strategy to train multilayer perceptron controllers for the Atari Space Invaders task, the author analyzes the effects of weight representation and evolutionary hyperparameters. Experimental results show that ternary-weight networks achieved higher average performance than binary-weight networks with identical architectures, although the difference was not statistically significant. Additionally, a larger population size combined with fewer generations was found to be more effective than smaller populations with longer training durations, consistent with prior findings. These results suggest that population size plays a critical role in compensating for the limited global search capability of ES.