# Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing

arXiv: https://arxiv.org/abs/1809.09260 · HTML: https://mlsanity.com/paper/1809.09260/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control](https://mlsanity.com/paper/2412.01034/) (2024)
- [Saliency-Aware Quantized Imitation Learning for Efficient Robotic Control](https://mlsanity.com/paper/2505.15304/) (2025)

## Abstract (arXiv)

Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on low-power neuromorphic hardware by converting a sequence of game frames into discrete actions.