How can LLMs improve robot teamwork?
Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation
December 31, 2024
https://arxiv.org/pdf/2412.20397This paper introduces a decentralized learning-based framework for coordinating multiple robots to complete tasks that require collaboration, especially in dynamic environments where new tasks constantly appear. It uses a modified version of Multi-Agent Proximal Policy Optimization (MAPPO), a reinforcement learning technique, to train a shared policy among the robots. Robots communicate their intended actions and revise their plans based on local observations and shared information, enabling efficient coalition formation for complex tasks.
Key points for LLM-based multi-agent systems:
- Decentralized Control with Partial Observability: The framework allows agents to operate independently with limited information, mirroring real-world scenarios and reducing reliance on centralized coordination. This is analogous to how LLMs in a multi-agent system might operate with limited access to the overall system state.
- Intention Sharing for Coordination: Robots share their planned actions to facilitate cooperation on complex tasks. This is directly applicable to LLM agents, where sharing intended actions or plans via natural language can improve coordination.
- Dynamic Task Allocation and Revision: The system handles constantly changing tasks and priorities, requiring agents to adapt and revise their plans dynamically. This is crucial for LLM-based multi-agent systems operating in real-time, dynamic environments.
- Scalability and Generalizability: The framework is demonstrated to work efficiently with a large number of agents and in diverse task environments, highlighting its potential for large-scale LLM-based multi-agent applications.
- Abstract Action Spaces: The use of spatial action maps allows the policy to focus on high-level decision-making (task selection), leaving low-level control (navigation) to a separate module. This abstraction aligns with the potential of LLMs to handle high-level reasoning and planning in multi-agent systems.