论文标题
多发双边谈判的深度学习策略模板
Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation
论文作者
论文摘要
我们研究如何利用战略模板的概念来学习多发双边谈判的策略。每个策略模板都由一组可解释的参数化策略组成，这些策略用于随时决定最佳动作。我们在整个参与者批评的架构中使用深度强化学习来估计阈值实用程序的策略参数值，何时接受要约以及如何产生新的出价。这与现有工作形成鲜明对比，后者仅估计这些策略的阈值实用程序。我们通过使用“教师策略”收集的数据集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，我们为可以适应不同谈判领域的多发谈论构建了自动化代理，而无需预先编程。我们从经验上表明，我们的工作在个人和社会效率方面都优于最先进的。
We study how to exploit the notion of strategy templates to learn strategies 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. We use deep reinforcement learning throughout an actor-critic architecture to estimate the tactic parameter values for a threshold utility, when to accept an offer and how to generate a new bid. This contrasts with existing work that only estimates the threshold utility for those tactics. We pre-train the strategy by supervision from the dataset collected using "teacher strategies", thereby decreasing the exploration time required for learning during negotiation. 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. We empirically show that our work outperforms the state-of-the-art in terms of the individual as well as social efficiency.
