How can PPO train UAVs to explore?
On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration
September 18, 2024
https://arxiv.org/pdf/2409.11058This paper investigates the use of multi-agent reinforcement learning (specifically the PPO algorithm) for coordinating multiple UAVs in exploration tasks within unknown environments.
The key points relevant to LLM-based multi-agent systems are the use of centralized training and decentralized execution, the exploration of using LSTM networks to handle the temporal aspect of UAV movement, and the importance of carefully designed reward functions and hyperparameter tuning for efficient exploration and policy convergence.