A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Abstract
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning-based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
Cite
@article{arxiv.2001.11785,
title = {A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation},
author = {Pallavi Bagga and Nicola Paoletti and Bedour Alrayes and Kostas Stathis},
journal= {arXiv preprint arXiv:2001.11785},
year = {2020}
}