# A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

arXiv: https://arxiv.org/abs/2001.11785 · HTML: https://mlsanity.com/paper/2001.11785/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [An Autonomous Negotiating Agent Framework with Reinforcement Learning Based Strategies and Adaptive Strategy Switching Mechanism](https://mlsanity.com/paper/2102.03588/) (2021)
- [A Contextual Combinatorial Bandit Approach to Negotiation](https://mlsanity.com/paper/2407.00567/) (2024)
- [AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond Human Understanding](https://mlsanity.com/paper/2503.01063/) (2025)
- [Anchor-and-Resume Concession Under Dynamic Pricing for LLM-Augmented Freight Negotiation](https://mlsanity.com/paper/2604.20732/) (2026)
- [The Foreign Policy AI Evaluation Gap](https://mlsanity.com/paper/2607.02955/) (2026)
- [Parallelism or Concession? Concurrency-Aware Procurement Negotiation for Agentic Commerce](https://mlsanity.com/paper/2610.06017/) (2026)

## Abstract (arXiv)

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning-based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.