# Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation

arXiv: https://arxiv.org/abs/2201.02455 · HTML: https://mlsanity.com/paper/2201.02455/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Towards General Negotiation Strategies with End-to-End Reinforcement Learning](https://mlsanity.com/paper/2406.15096/) (2024)
- [Towards Explainable Strategy Templates using NLP Transformers](https://mlsanity.com/paper/2311.14061/) (2023)
- [RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots](https://mlsanity.com/paper/2507.22664/) (2025)

## Abstract (arXiv)

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.