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Application of ensemble of neural networks is becoming an imminent tool foradvancing the state-of-the-art in 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 traininglarge-scale systems. In this paper, we propose DNS: a Determinantal PointProcess based Neural Network Sampler that specifically uses k-dpp to sample asubset of neural networks for backpropagation at every training step thussignificantly reducing the training time and computation cost. We integratedDNS in REDQ for continuous control tasks and evaluated on MuJoCo environments.Our experiments show that DNS augmented REDQ outperforms baseline REDQ in termsof average cumulative reward and achieves this using less than 50% computationwhen measured in FLOPS.

DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning