MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning
Summary: arXiv:2604.05943v1 Announce Type: new
Abstract: 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).
Introduction
The landscape of artificial intelligence is rapidly evolving, particularly in the domain of multi-agent reinforcement learning (MARL). Traditional methods often necessitate the development of specialized models tailored to individual tasks, which can be resource-intensive and time-consuming. The introduction of MARL-GPT marks a significant shift in this paradigm, allowing for a unified approach to tackle various MARL challenges efficiently.
Key Features of MARL-GPT
- Unified Model: MARL-GPT leverages a single GPT-based architecture, eliminating the need for task-specific models.
- Offline Reinforcement Learning: The model utilizes offline reinforcement learning, enabling it to train on vast amounts of expert data.
- Scalability: With extensive training on expert trajectories (400M for SMACv2, 100M for GRF, and 1B for POGEMA), MARL-GPT demonstrates impressive scalability.
- Transformer-Based Observation Encoder: The innovative use of a transformer-based observation encoder allows for robust performance without the need for fine-tuning.
Performance Evaluation
To validate its effectiveness, MARL-GPT was subjected to rigorous testing across several complex environments such as:
- StarCraft Multi-Agent Challenge (SMAC): A benchmark for evaluating multi-agent systems in real-time strategy games.
- Google Research Football (GRF): A challenging environment for testing decision-making in dynamic settings.
- POGEMA: A novel multi-agent task that emphasizes coordination among agents.
In each of these environments, MARL-GPT consistently achieved competitive performance when compared to specialized baselines. These results underscore the potential of adopting a multi-task model architecture in MARL.
Conclusion
The advent of MARL-GPT signals a promising direction for future research in multi-agent reinforcement learning. By demonstrating that a single model can proficiently handle a variety of tasks, MARL-GPT lays the groundwork for a new standard in the field. As artificial intelligence continues to advance, the implications of such a foundational model could be profound, influencing not only MARL but also broader applications in AI.
Future Prospects
Looking ahead, the development of MARL-GPT opens up exciting opportunities for further exploration and innovation in multi-agent systems. Researchers are encouraged to investigate the scalability of this model in even more complex environments, potentially leading to groundbreaking applications across various industries.