  Effective collaboration in multi-agent systems requires communicating goals
and intentions between agents. Current agent frameworks often suffer from
dependencies on single-agent execution and lack robust inter-module
communication, frequently leading to suboptimal multi-agent reinforcement
learning (MARL) policies and inadequate task coordination. To address these
challenges, we present a framework for training large language models (LLMs) as
collaborative agents to enable coordinated behaviors in cooperative MARL. Each
agent maintains a private intention consisting of its current goal and
associated sub-tasks. Agents broadcast their intentions periodically, allowing
other agents to infer coordination tasks. A propagation network transforms
broadcast intentions into teammate-specific communication messages, sharing
relevant goals with designated teammates. The architecture of our framework is
structured into planning, grounding, and execution modules. During execution,
multiple agents interact in a downstream environment and communicate intentions
to enable coordinated behaviors. The grounding module dynamically adapts
comprehension strategies based on emerging coordination patterns, while
feedback from execution agents influnces the planning module, enabling the
dynamic re-planning of sub-tasks. Results in collaborative environment
simulation demonstrate intention propagation reduces miscoordination errors by
aligning sub-task dependencies between agents. Agents learn when to communicate
intentions and which teammates require task details, resulting in emergent
coordinated behaviors. This demonstrates the efficacy of intention sharing for
cooperative multi-agent RL based on LLMs.
