ANEGMA: an automated negotiation model for e-markets

作者:Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis

摘要

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.

论文关键词:Multi-agent negotiation, Deep reinforcement learning, Concurrent bilateral negotiation

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论文官网地址:https://doi.org/10.1007/s10458-021-09513-x