A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 297-303. https://doi.org/10.24963/ijcai.2020/42
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.
Agent-based and Multi-agent Systems: Agreement Technologies: Negotiation and Contract-Based Systems
Machine Learning Applications: Applications of Reinforcement Learning
Machine Learning Applications: Applications of Supervised Learning