How can local info improve robot swarm task allocation?
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation
December 2, 2024
https://arxiv.org/pdf/2411.19526This paper proposes a new method (LIA_MADDPG) for assigning tasks to a swarm of robots in a dynamic environment where tasks and robots are constantly moving. It uses a decentralized, multi-agent reinforcement learning approach, where each robot learns how to choose tasks efficiently by collaborating with nearby robots.
Key points relevant to LLM-based multi-agent systems:
- Decentralized Control: Each robot acts as an independent agent with its own observations and decision-making process. This mirrors the distributed nature of many LLM-based multi-agent applications.
- Local Information Aggregation (LIA): Agents only consider information from nearby or relevant agents, reducing the complexity of communication and computation. This is crucial for scaling multi-agent LLM systems, where global communication can be a bottleneck.
- Policy Improvement: Robots dynamically refine their task selection strategies during execution, enabling adaptation to changing conditions. This continuous learning and adaptation are important for LLM-based agents that need to improve their performance over time.
- Scalability: The method demonstrates superior performance compared to traditional methods, especially as the number of robots and tasks increases. This scalability is a significant consideration when deploying complex LLM-based multi-agent applications.
- Sim-to-real transition: The approach is validated in a high-fidelity physics simulator, highlighting the potential for deployment in real-world scenarios. This is crucial for practical applications of LLM-based multi-agent systems.