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DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Hassam Sheikh · Kizza Nandyose Frisbee · mariano phielipp

Wed Jul 20 10:40 AM -- 10:45 AM (PDT) @ Room 307

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

Author Information

Hassam Sheikh (Intel Labs)
Kizza Nandyose Frisbee (Intel Corporation)
mariano phielipp (Intel AI Labs)

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