The research field of automated negotiation has a long history of designing agents that can negotiate with other agents. Such negotiation strategies are traditionally based on manual design and heuristics. More recently, reinforcement learning approaches have also been used to train agents to negotiate. However, negotiation problems are diverse, causing observation and action dimensions to change, which cannot be handled by default linear policy networks. Previous work on this topic has circumvented this issue either by fixing the negotiation problem, causing policies to be non-transferable between negotiation problems or by abstracting the observations and actions into fixed-size representations, causing loss of information and expressiveness due to feature design. We developed an end-to-end reinforcement learning method for diverse negotiation problems by representing observations and actions as a graph and applying graph neural networks in the policy. With empirical evaluations, we show that our method is effective and that we can learn to negotiate with other agents on never-before-seen negotiation problems. Our result opens up new opportunities for reinforcement learning in negotiation agents.

我们利用图形表示观察和行为，并在策略中应用图神经网络，开发了一种用于多样化谈判问题的端到端强化学习方法，通过实证评估，我们表明我们的方法是有效的，我们可以学会与以前未见过的谈判问题中的其他代理进行谈判，这一结果为强化学习在谈判代理中开辟了新的机会。

基于端到端强化学习的通用谈判策略