Liu, Yuchen 
ORCID: 0009-0007-1525-7676, Man, Ka Lok ORCID: 0000-0002-5787-4716, Li, Gangmin ORCID: 0000-0003-4006-7472, Payne, Terry R ORCID: 0000-0002-0106-8731 and Yue, Yong ORCID: 0000-0001-7695-4538
(2024)
Evaluating and Selecting Deep Reinforcement Learning Models for OptimalDynamic Pricing: A Systematic Comparison of PPO, DDPG, and SAC.
In: CCEAI 2024: 2024 8th International Conference on Control Engineering and Artificial Intelligence, 2024-1-26 - 2024-1-28, Shanghai, China.
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Evaluating and Selecting Deep Reinforcement Learning Models for Optimal Dynamic Pricing A Systematic Comparison of PPO, DDPG, and SAC__Camera Ready.pdf - Author Accepted Manuscript Download (826kB) | Preview |
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.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 16 Nov 2023 08:24 |
| Last Modified: | 26 Mar 2024 18:40 |
| DOI: | 10.1145/3640824.3640871 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3176825 |
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