Deep Reinforcement Learning Framework with Representation Learning for Concurrent Negotiation

Ryoga Miyajima, Katsuhide Fujita

2024

Abstract

In the field of automated negotiation, significant attention has been paid to methods for learning negotiation strategies using reinforcement learning. However, in concurrent negotiation, where negotiation proceeds with multiple counterparties with various strategies in parallel, it is difficult to consider the differences in the strategies of counterparties using the conventional formulation in which the state is defined using the bids of both counterparties. In this study, we propose a reinforcement learning framework for learning negotiation strategies that considers the strategy models of the negotiation partners in concurrent negotiations. Strategy modeling is realized using embeddings with a representation function based on the unsupervised learning of generative–discriminative representations from negotiation log data. Through evaluation experiments, we show the performance of the representation function in identifying the strategies of negotiation partners and the effectiveness of introducing the representation function into the reinforcement learning of negotiation strategies.

Paper Citation

in Harvard Style

Miyajima R. and Fujita K. (2024). Deep Reinforcement Learning Framework with Representation Learning for Concurrent Negotiation. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 231-239. DOI: 10.5220/0012336000003636

in Bibtex Style

@conference{icaart24,
author={Ryoga Miyajima and Katsuhide Fujita},
title={Deep Reinforcement Learning Framework with Representation Learning for Concurrent Negotiation},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={231-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012336000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Deep Reinforcement Learning Framework with Representation Learning for Concurrent Negotiation
SN - 978-989-758-680-4
AU - Miyajima R.
AU - Fujita K.
PY - 2024
SP - 231
EP - 239
DO - 10.5220/0012336000003636
PB - SciTePress