A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis

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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.
Original languageEnglish
Number of pages7
Publication statusPublished - Jul 2020
Event29th International Joint Conference on Artificial Intelligence - Yokohama, Japan
Duration: 11 Jul 202017 Jul 2020


Conference29th International Joint Conference on Artificial Intelligence
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