# Efficient Generation of Diverse Cooperative Agents with World Models

arXiv: https://arxiv.org/abs/2506.07450 · HTML: https://mlsanity.com/paper/2506.07450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Latent dynamics models and model-based RL, one of 11 directions in World models.
- Builds on 28 of its 37 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Placed in

- World models / [Latent dynamics models and model-based RL](https://mlsanity.com/wm/latent-dynamics-models-and-model-based-rl/)

## Builds on (most cited first, 28 of 28)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Off-Belief Learning](https://mlsanity.com/paper/2103.04000/) (2021)
- [Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2112.11701/) (2021)
- [Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased](https://mlsanity.com/paper/2302.01605/) (2023)
- [PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2301.06387/) (2023)
- [ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination](https://mlsanity.com/paper/2310.05208/) (2023)
- [Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination](https://mlsanity.com/paper/2201.12436/) (2022)
- [The Benefits of Model-Based Generalization in Reinforcement Learning](https://mlsanity.com/paper/2211.02222/) (2022)
- [Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization](https://mlsanity.com/paper/2403.10967/) (2024)
- [Diverse Conventions for Human-AI Collaboration](https://mlsanity.com/paper/2310.15414/) (2023)
- [Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents](https://mlsanity.com/paper/2308.09595/) (2023)
- [Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2306.03034/) (2023)
- [A Hierarchical Approach to Population Training for Human-AI Collaboration](https://mlsanity.com/paper/2305.16708/) (2023)
- [MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.06011/) (2023)

## Built on by (most cited first, 1 of 1)

- [MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind](https://mlsanity.com/paper/2603.00808/) (2026)

## Abstract (arXiv)

A major bottleneck in the training process for Zero-Shot Coordination (ZSC) agents is the generation of partner agents that are diverse in collaborative conventions. Current Cross-play Minimization (XPM) methods for population generation can be very computationally expensive and sample inefficient as the training objective requires sampling multiple types of trajectories. Each partner agent in the population is also trained from scratch, despite all of the partners in the population learning policies of the same coordination task. In this work, we propose that simulated trajectories from the dynamics model of an environment can drastically speed up the training process for XPM methods. We introduce XPM-WM, a framework for generating simulated trajectories for XPM via a learned World Model (WM). We show XPM with simulated trajectories removes the need to sample multiple trajectories. In addition, we show our proposed method can effectively generate partners with diverse conventions that match the performance of previous methods in terms of SP population training reward as well as training partners for ZSC agents. Our method is thus, significantly more sample efficient and scalable to a larger number of partners.