TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learning
This addresses the pursuit-evasion challenge for multi-robot systems, offering a scalable solution for encirclement tasks, though it appears incremental as it builds on existing RL methods with transformer enhancements.
The paper tackles the multi-target encirclement problem in large-scale multi-robot systems by proposing a Transformer-Enhanced Reinforcement Learning (TERL) framework, which achieves a 100% success rate and outperforms existing methods in success rate and task completion time while generalizing from small to large scales without retraining.
Pursuit-evasion (PE) problem is a critical challenge in multi-robot systems (MRS). While reinforcement learning (RL) has shown its promise in addressing PE tasks, research has primarily focused on single-target pursuit, with limited exploration of multi-target encirclement, particularly in large-scale settings. This paper proposes a Transformer-Enhanced Reinforcement Learning (TERL) framework for large-scale multi-target encirclement. By integrating a transformer-based policy network with target selection, TERL enables robots to adaptively prioritize targets and safely coordinate robots. Results show that TERL outperforms existing RL-based methods in terms of encirclement success rate and task completion time, while maintaining good performance in large-scale scenarios. Notably, TERL, trained on small-scale scenarios (15 pursuers, 4 targets), generalizes effectively to large-scale settings (80 pursuers, 20 targets) without retraining, achieving a 100% success rate. The code and demonstration video are available at https://github.com/ApricityZ/TERL.