In real applications of Reinforcement Learning (RL), such as robotics, low latency and energy efficient inference is very desired. The use of sparsity and pruning for optimizing Neural Network inference, and particularly to improve energy and latency efficiency, is a standard technique. In this work, we perform a systematic investigation of applying these optimization techniques for different RL algorithms in different RL environments, yielding up to a 400-fold reduction in the size of neural networks.

翻译：在强化学习（RL）的实际应用（如机器人领域）中，低延迟与高能效推理是迫切需求。利用稀疏性与剪枝技术优化神经网络推理，尤其是提升能量与延迟效率，已成为标准方法。本研究系统探究了如何将这些优化技术应用于不同RL环境中的各类RL算法，实现了神经网络规模最高达400倍的缩减。