# From Pixels to Cooperation Multi Agent Reinforcement Learning based on Multimodal World Models

arXiv: https://arxiv.org/abs/2511.01310 · HTML: https://mlsanity.com/paper/2511.01310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Latent dynamics models and model-based RL, one of 11 directions in World models.
- First posted on arXiv 3 Nov 2025.

## Placed in

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

## Abstract (arXiv)

Learning cooperative multi-agent policies directly from high-dimensional, multimodal sensory inputs like pixels and audio (from pixels) is notoriously sample-inefficient. Model-free Multi-Agent Reinforcement Learning (MARL) algorithms struggle with the joint challenge of representation learning, partial observability, and credit assignment. To address this, we propose a novel framework based on a shared, generative Multimodal World Model (MWM). Our MWM is trained to learn a compressed latent representation of the environment's dynamics by fusing distributed, multimodal observations from all agents using a scalable attention-based mechanism. Subsequently, we leverage this learned MWM as a fast, "imagined" simulator to train cooperative MARL policies (e.g., MAPPO) entirely within its latent space, decoupling representation learning from policy learning. We introduce a new set of challenging multimodal, multi-agent benchmarks built on a 3D physics simulator. Our experiments demonstrate that our MWM-MARL framework achieves orders-of-magnitude greater sample efficiency compared to state-of-the-art model-free MARL baselines. We further show that our proposed multimodal fusion is essential for task success in environments with sensory asymmetry and that our architecture provides superior robustness to sensor-dropout, a critical feature for real-world deployment.