论文标题
一种深厚的增强学习方法，以进行双边谈判
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
论文作者
论文摘要
我们提出了一种新颖的谈判模型，该模型使代理商可以学习如何在未知和动态电子市场中并发双边谈判中进行谈判。该代理使用与无模型的强化学习一起使用参与者批评的结构来学习以深神经网络表示的策略。我们通过从合成市场数据中的监督来预先培训该策略，从而减少了在谈判过程中学习所需的探索时间。结果，我们可以为并发谈判构建自动化代理，这些谈判可以适应不同的电子市场设置，而无需预先编程。我们的实验评估表明，我们基于深的学习的代理在一对一并发的双边谈判中，在一系列电子市场设置中，我们的基于学习的代理的表现优于两种现有的众所周知的谈判策略。
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.
