A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2201.02455v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.02455v1">arXiv:2201.02455v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fhqskwawljeyzabxhayhrizk6m">fatcat:fhqskwawljeyzabxhayhrizk6m</a> </span>
more »... 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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