The application of an ensemble of neural networks is becoming an imminent tool for advancing state-of-the-art deep reinforcement learning algorithms. However, training these large numbers of neural networks in the ensemble has anexceedingly high computation cost which may become a hindrance in training large-scale systems. In this paper, we propose DNS: a DeterminantalPoint Process based Neural Network Sampler that specifically uses k-DPP to sample a subset of neural networks for backpropagation at every training step thus significantly reducing the training time and computation cost. We integrated DNS in REDQ for continuous control tasks and evaluated on MuJoCo environments. Our experiments show that DNS augmented REDQ matches the baseline REDQ in terms of average cumulative reward and achieves this using less than 50% computation when measured in FLOPS. The code is available at https://github.com/IntelLabs/DNS