# Multi-Issue Bargaining With Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.07788 · HTML: https://mlsanity.com/paper/2002.07788/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Fairness-Aware Meta-Learning via Nash Bargaining](https://mlsanity.com/paper/2406.07029/) (2024)
- [Training Language Models for Bilateral Trade with Private Information](https://mlsanity.com/paper/2604.16472/) (2026)
- [Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing](https://mlsanity.com/paper/2310.17458/) (2023)
- [Distilling Bayesian Belief States into Language Models for Auditable Negotiation](https://mlsanity.com/paper/2605.04507/) (2026)
- [How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions](https://mlsanity.com/paper/2606.29457/) (2026)

## Abstract (arXiv)

Negotiation is a process where agents aim to work through disputes and maximize their surplus. As the use of deep reinforcement learning in bargaining games is unexplored, this paper evaluates its ability to exploit, adapt, and cooperate to produce fair outcomes. Two actor-critic networks were trained for the bidding and acceptance strategy, against time-based agents, behavior-based agents, and through self-play. Gameplay against these agents reveals three key findings. 1) Neural agents learn to exploit time-based agents, achieving clear transitions in decision preference values. The Cauchy distribution emerges as suitable for sampling offers, due to its peaky center and heavy tails. The kurtosis and variance sensitivity of the probability distributions used for continuous control produce trade-offs in exploration and exploitation. 2) Neural agents demonstrate adaptive behavior against different combinations of concession, discount factors, and behavior-based strategies. 3) Most importantly, neural agents learn to cooperate with other behavior-based agents, in certain cases utilizing non-credible threats to force fairer results. This bears similarities with reputation-based strategies in the evolutionary dynamics, and departs from equilibria in classical game theory.