How can MARL optimize drone mission execution with limited battery?
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks
October 31, 2024
https://arxiv.org/pdf/2410.22578This paper proposes a multi-agent reinforcement learning (MARL) model for managing a fleet of drones with limited battery life to complete a mission consisting of multiple tasks with varying locations and durations. Each drone uses a Deep Q-Network (DQN) to learn optimal actions, balancing task completion with energy conservation for travel and hovering. A shared reward function encourages collaboration.
Key points for LLM-based multi-agent systems:
- Decentralized control: Each drone acts autonomously based on its local observations and learned policy.
- Shared reward function: Promotes collaboration towards a global objective.
- State and Action Space: Demonstrates defining a discrete state and action space for agents in a simulated environment, a crucial step for MARL applications. This can be adapted for LLMs by defining appropriate token-based state and action representations.
- Adaptability: The model's ability to handle varying task locations and durations offers insights into building robust multi-agent systems capable of adapting to dynamic environments, a common requirement for LLM-based agents interacting with real-world data.
- Scalability Challenges: The paper acknowledges limitations and points towards future research on scaling the model for larger grids and 3D environments. This is directly relevant to LLM-based systems, which often struggle with computational complexity in multi-agent setups.
- Collaboration vs. Competition: Future work explores the potential of individual reward functions, highlighting the critical design choice between collaborative and competitive learning in multi-agent LLM systems.