This study investigates the application of Evolution Strategy (ES) to train binary neural network controllers for the Atari game Space Invaders, extending previous work for control tasks such as Pendulum and Acrobot. Unlike conventional networks using real-valued weights, this approach represents connection weights using binary values from the set {-1, 1}. Experimental results evaluate the performance of multilayer perceptrons (MLPs) with varying numbers of hidden units and weight bit precision (1-bit vs. 64-bit). Key findings indicate that 1-bit MLPs achieve comparable performance to 64-bit MLPs. Moreover, performance with only 2 hidden units is comparable to those with 4, 8, and 16 hidden units, suggesting that binary quantization may not necessitate increased model complexity. Additionally, results demonstrate that increasing the number of offspring per generation enhances ES effectiveness more than increasing the number of generations. These findings highlight the potential of binary-weight neural networks for efficient and effective reinforcement learning in resource-constrained settings.