# Markovian Embeddings for Coalitional Bargaining Games

arXiv: https://arxiv.org/abs/2306.11104 · HTML: https://mlsanity.com/paper/2306.11104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Game-Theoretic Multiagent Reinforcement Learning](https://mlsanity.com/paper/2011.00583/) (2020)
- [Negotiating Team Formation Using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.10380/) (2020)

## Abstract (arXiv)

We examine the Markovian properties of coalition bargaining games, in particular, the case where past rejected proposals cannot be repeated. We propose a Markovian embedding with filtrations to render the sates Markovian and thus, fit into the framework of stochastic games.