TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 36 references that are in the map. First posted on arXiv 16 Mar 2025.
Builds on
15 of 36 references.
References · 15
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3 more
- 2022Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learningcited by 6
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Citing papers per year: 2025: 1.
1 paper.
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- 2026Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models
- 2026Transformer-based Multi-agent Reinforcement Learning for Separation Assurance in Structured and Unstructured Airspaces
- 2024Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
- 2024Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Games
- 2025Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment
- 2024FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning