From Pixels to Cooperation Multi Agent Reinforcement Learning based on Multimodal World Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912700825600000 |
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| author | Akin, Sureyya Srivastava, Kavita Kapoor, Prateek B. Sethi, Pradeep G. Patel, Sunita Q. Srivastava, Rahu |
| author_facet | Akin, Sureyya Srivastava, Kavita Kapoor, Prateek B. Sethi, Pradeep G. Patel, Sunita Q. Srivastava, Rahu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Pixels to Cooperation Multi Agent Reinforcement Learning based on Multimodal World Models Akin, Sureyya Srivastava, Kavita Kapoor, Prateek B. Sethi, Pradeep G. Patel, Sunita Q. Srivastava, Rahu Multiagent Systems 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. |
| title | From Pixels to Cooperation Multi Agent Reinforcement Learning based on Multimodal World Models |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2511.01310 |