  Application of ensemble of neural networks is becoming an imminent tool for
advancing the state-of-the-art in deep reinforcement learning algorithms.
However, training these large numbers of neural networks in the ensemble has an
exceedingly high computation cost which may become a hindrance in training
large-scale systems. In this paper, we propose DNS: a Determinantal Point
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 outperforms baseline REDQ in terms
of average cumulative reward and achieves this using less than 50% computation
when measured in FLOPS.
