Deep Reinforcement Learning for Auctions: Evaluating Bidding Strategies Effectiveness and Convergence

Luis Eduardo Craizer, Edward Hermann, Moacyr Alvim Silva

2025

Abstract

This paper extends our previous work on using deep reinforcement learning, specifically the MADDPG algorithm, to analyze and optimize bidding strategies across different auction scenarios. Our current research aims to empirically verify whether the agents’ optimal policies, achieved after model convergence, approach a near-Nash equilibrium in various auction settings. We propose a novel empirical strategy that compares the learned policy of each agent, derived through the deep reinforcement learning algorithm, with an optimal bid strategy obtained via an exhaustive search based on bid points from other participants. This comparative analysis encompasses different auctions, revealing various equilibrium scenarios. Our findings contribute to a deeper understanding of decision-making dynamics in multi-agent environments and provide valuable insights into the robustness of deep reinforcement learning techniques in auction theory.

Paper Citation

in Harvard Style

Craizer L., Hermann E. and Silva M. (2025). Deep Reinforcement Learning for Auctions: Evaluating Bidding Strategies Effectiveness and Convergence. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 367-376. DOI: 10.5220/0013146400003890

in Bibtex Style

@conference{icaart25,
author={Luis Craizer and Edward Hermann and Moacyr Silva},
title={Deep Reinforcement Learning for Auctions: Evaluating Bidding Strategies Effectiveness and Convergence},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={367-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013146400003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Deep Reinforcement Learning for Auctions: Evaluating Bidding Strategies Effectiveness and Convergence
SN - 978-989-758-737-5
AU - Craizer L.
AU - Hermann E.
AU - Silva M.
PY - 2025
SP - 367
EP - 376
DO - 10.5220/0013146400003890
PB - SciTePress