Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation

We propose the notion of deep reinforcement learning-based strategy templates for multi-issue bilateral negotiation. Each strategy template consists of a set of interpretable parameterized tactics that are used to decide an optimal action at any time. This contrasts with existing work that only estimates the threshold utility for those tactics that require it. As a result, we build automated agents for multi-issue negotiations that can adapt to different negotiation domains without the need to be pre-programmed.

Authors

• Pallavi Bagga
• Nicola Paoletti
• Kostas Stathis Royal Holloway University of London

Keywords

• Multi-Issue Negotiation
• Deep Reinforcement Learning
• Bilateral
• Automated Negotiation
• Interpretable Negotiation Strategies