How can I train UAVs to navigate unseen environments?
DTPPO: Dual-Transformer Encoder-based Proximal Policy Optimization for Multi-UAV Navigation in Unseen Complex Environments
This paper proposes DTPPO, a novel method for coordinating multiple drones in complex environments using deep reinforcement learning. DTPPO leverages a Dual-Transformer architecture, composed of Spatial and Temporal Transformers, to enhance inter-drone collaboration and model dynamic environmental changes.
Importantly for LLM-based systems, DTPPO demonstrates strong zero-shot transfer capabilities. This means it can be trained on a set of scenarios and then successfully navigate new, unseen environments without requiring retraining. This is achieved by learning generalizable navigation strategies from the spatial and temporal patterns encoded by the transformers. DTPPO's ability to transfer knowledge to new scenarios, coupled with its enhanced safety and efficiency in obstacle-rich environments, makes it particularly promising for real-time multi-agent applications.