面向多议题双边谈判的深度学习策略模板
Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation
摘要：
我们研究如何利用策略模板的概念来学习多议题双边谈判策略。每个策略模板由一组可解释的参数化策略组成，用于在任何时候决定最佳行动。我们在整个行动者-批评者架构中使用深度强化学习来估计阈值效用的策略参数值，何时接受报价以及如何生成新的出价。这与现有的工作，只估计这些策略的阈值效用。我们通过使用“教师策略”收集的数据集进行监督来预训练策略，从而减少谈判过程中学习所需的探索时间。因此，我们建立自动代理多问题的谈判，可以适应不同的谈判域，而不需要预先编程。我们的经验表明，我们的工作优于国家的最先进的个人以及社会效率方面。
英文摘要：
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.
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