Guobin Zhu

Multi-Task Reinforcement Learning of Drone Aerobatics by Exploiting Geometric Symmetries

LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation

Although Multi-Agent Reinforcement Learning (MARL) is effective for complex multi-robot tasks, it suffers from low sample efficiency and requires iterative manual reward tuning. Large Language Models (LLMs) have shown promise in single-robot settings, but their application in multi-robot systems rem

Multi-Task Multi-Agent Reinforcement Learning via Skill Graphs

Multi-task multi-agent reinforcement learning (M T-MARL) has recently gained attention for its potential to enhance MARL's adaptability across multiple tasks. However, it is challenging for existing multi-task learning methods to handle complex problems, as they are unable to handle unrelated tasks