DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making
Building scalable and reusable multi-agent decision policies from offline datasets remains a challenge in offline multi-agent reinforcement learning (MARL), as existing methods often rely on fixed observation formats and action spaces that limit generalization. In contrast, large language models (LLMs) offer a flexible modeling interface that can naturally accommodate heterogeneous observations and actions. Motivated by this, we propose the Decision Language Model (DLM), which formulates multi-agent decision making as a dialogue-style sequence prediction problem under the centralized training with decentralized execution paradigm. DLM is trained in two stages: a supervised fine-tuning pha...
| Authors | Zhuohui Zhang, Bin Cheng, Bin He |
|---|---|
| Primary category | cs.MA |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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