Songjun Huang

LLM-Based Multi-Agent Reinforcement Learning: Challenges and Future Directions

TF-FACE: Time-Frequency Fusion Learning via Frequency-Domain Adaptive and Controllable Enhancement for Trajectory Prediction

Accurately predicting the future trajectories of traffic participants is critical for safe, efficient, and human-friendly autonomous driving. Existing learning-based trajectory prediction methods are predominantly time-domain and insufficiently exploit latent frequency information, which limits thei…

LLM-Based Multi-Agent Decision-Making: Challenges and Future Directions

In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poetry writing, among others. Although research on LLM-as-an-agent has shown that LLM can be applied to Decision-Making (DM) and achieve decent res

Retrieval-Augmented Hierarchical in-Context Reinforcement Learning and Hindsight Modular Reflections for Task Planning with LLMs

Large Language Models (LLMs) have demonstrated remarkable abilities in various language tasks, making them promising candidates for decision-making in robotics. Inspired by Hierarchical Reinforcement Learning (HRL), we propose Retrieval-Augmented Hierarchical in-context reinforcement Learning (RAHL)

HMAAC: Hierarchical Multi-Agent Actor-Critic for Aerial Search with Explicit Coordination Modeling

Unmanned Aerial Vehicles (UAVs) have become prevalent in Search-And-Rescue (SAR) missions. However, existing solutions to the control and coordination of UAV s are mostly limited to specific environments and are not robust to handle unreliable/unstable communications. To deal with these challenges,…