Skip to search boxSkip to navigationSkip to main content

Evaluating and selecting deep reinforcement learning models for OptimalDynamic pricing: a systematic comparison of PPO, DDPG, and SAC

  • Yuchen Liu
    ,
  • Ka Lok Man(corresponding author)
    ,
  • Gangmin Li
    ,
  • Terry R. Payne
    ,
  • Yong Yue
*Corresponding author for this work
  • Xi'an Jiaotong-Liverpool University
    ,
  • University of Liverpool
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Given the plethora of available solutions, choosing an appropriate Deep Reinforcement Learning (DRL) model for dynamic pricing poses a significant challenge for practitioners. While many DRL solutions claim superior performance, there lacks a standardized framework for their evaluation. Addressing this gap, we introduce a novel framework and a set of metrics to select and assess DRL models systematically. To validate the utility of our framework, we critically compared three representative DRL models, emphasizing their performance in dynamic pricing tasks. Further ensuring the robustness of our assessment, we benchmarked these models against a well-established human agent policy. The DRL model that emerged as the most effective was rigorously tested on an Amazon dataset, demonstrating a notable performance boost of 5.64%. Our findings underscore the value of our proposed metrics and framework in guiding practitioners towards the most suitable DRL solution for dynamic pricing.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 215-219 (5 pages)

Publication milestones

  • Published - 08/03/2024

Publication status

Published - 08/03/2024

Publisher

Association for Computing Machinery, United States

Publication series

  • Publication series name: ACM International Conference Proceeding Series

ISBN (Print)

9798400707971

ISBN (Electronic)

9798400707971

Publication IDs

  • handle.net: 10547/626226
  • Scopus: 85188251454

Host publication title

Proceedings - 2024 8th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2024

Host publication editors

  • Wenqiang Zhang
  • Yong Yue
  • Marek Ogiela

Access to documents

Publication metrics

Metrics

Scopus
citations
21, opens in new tab

PlumX, opens in new tab

Captures
23
Citation count
21

Related Event

Title

8th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2024

Event type

Conference

Date

26/01/2024 - 28/01/2024

Location

ShanghaiChina