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<p>In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing Neural Network inference, and particularly to improve energy efficiency, latency and throughput, is a standard technique. In this work, we conduct a systematic investigation of the application of these optimization techniques with popular RL algorithms, specifically Deep Q-Network and Soft Actor Critic, in different RL environments, including MuJoCo and Atari, which yields up to a 400-fold reduction in the size of neural networks. This work presents a systematic study on the applicability limits of using pruning and quantization to optimize neural networks in RL tasks, with a perspective of deployment in hardware to reduce power consumption and latency, while increasing throughput.</p>

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Neural network compression for reinforcement learning tasks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月21日

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  • Dmitry A. Ivanov,
  • Denis A. Larionov,
  • Oleg V. Maslennikov,
  • Vladimir V. Voevodin

摘要

In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing Neural Network inference, and particularly to improve energy efficiency, latency and throughput, is a standard technique. In this work, we conduct a systematic investigation of the application of these optimization techniques with popular RL algorithms, specifically Deep Q-Network and Soft Actor Critic, in different RL environments, including MuJoCo and Atari, which yields up to a 400-fold reduction in the size of neural networks. This work presents a systematic study on the applicability limits of using pruning and quantization to optimize neural networks in RL tasks, with a perspective of deployment in hardware to reduce power consumption and latency, while increasing throughput.