Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.05943 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910109193469952 |
|---|---|
| author | Nesterova, Maria Kolosov, Mikhail Andreychuk, Anton Cherepanov, Egor Bulichev, Oleg Kovalev, Alexey Yakovlev, Konstantin Panov, Aleksandr Skrynnik, Alexey |
| author_facet | Nesterova, Maria Kolosov, Mikhail Andreychuk, Anton Cherepanov, Egor Bulichev, Oleg Kovalev, Alexey Yakovlev, Konstantin Panov, Aleksandr Skrynnik, Alexey |
| contents | Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this work, we propose a coherent methodology that makes it possible for a single GPT-based model to learn and perform well across diverse MARL environments and tasks, including StarCraft Multi-Agent Challenge, Google Research Football and POGEMA. Our method, MARL-GPT, applies offline reinforcement learning to train at scale on the expert trajectories (400M for SMACv2, 100M for GRF, and 1B for POGEMA) combined with a single transformer-based observation encoder that requires no task-specific tuning. Experiments show that MARL-GPT achieves competitive performance compared to specialized baselines in all tested environments. Thus, our findings suggest that it is, indeed, possible to build a multi-task transformer-based model for a wide variety of (significantly different) multi-agent problems paving the way to the fundamental MARL model (akin to ChatGPT, Llama, Mistral etc. in natural language modeling). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05943 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning Nesterova, Maria Kolosov, Mikhail Andreychuk, Anton Cherepanov, Egor Bulichev, Oleg Kovalev, Alexey Yakovlev, Konstantin Panov, Aleksandr Skrynnik, Alexey Artificial Intelligence Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this work, we propose a coherent methodology that makes it possible for a single GPT-based model to learn and perform well across diverse MARL environments and tasks, including StarCraft Multi-Agent Challenge, Google Research Football and POGEMA. Our method, MARL-GPT, applies offline reinforcement learning to train at scale on the expert trajectories (400M for SMACv2, 100M for GRF, and 1B for POGEMA) combined with a single transformer-based observation encoder that requires no task-specific tuning. Experiments show that MARL-GPT achieves competitive performance compared to specialized baselines in all tested environments. Thus, our findings suggest that it is, indeed, possible to build a multi-task transformer-based model for a wide variety of (significantly different) multi-agent problems paving the way to the fundamental MARL model (akin to ChatGPT, Llama, Mistral etc. in natural language modeling). |
| title | MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.05943 |