PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning

Dor Livne, Kobi Cohen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The recent success of deep neural networks (DNNs) for function approximation in reinforcement learning has triggered the development of Deep Reinforcement Learning (DRL) algorithms in various fields, such as robotics, computer games, natural language processing, computer vision, sensing systems, and wireless networking. Unfortunately, DNNs suffer from high computational cost and memory consumption, which limits the use of DRL algorithms in systems with limited hardware resources. In recent years, pruning algorithms have demonstrated considerable success in reducing the redundancy of DNNs in classification tasks. However, existing algorithms suffer from a significant performance reduction in the DRL domain. In this article, we develop the first effective solution to the performance reduction problem of pruning in the DRL domain, and establish a working algorithm, named Policy Pruning and Shrinking (PoPS), to train DRL models with strong performance while achieving a compact representation of the DNN. The framework is based on a novel iterative policy pruning and shrinking method that leverages the power of transfer learning when training the DRL model. We present an extensive experimental study that demonstrates the strong performance of PoPS using the popular Cartpole, Lunar Lander, Pong, and Pacman environments. Finally, we develop an open source software for the benefit of researchers and developers in related fields.

Original languageEnglish
Article number8962235
Pages (from-to)789-801
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Volume14
Issue number4
DOIs
StatePublished - 1 May 2020

Keywords

  • Deep reinforcement learning (DRL)
  • deep neural network (DNN)
  • pruning algorithms

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