Lab
Generalist Agents
Recently developed foundation models, such as large language models and multi-modal models, open great opportunities to build generally capable agents, combined with reinforcement learning. This project focuses on learning skills and foundation models and connecting them to build generalist agents. In the following, we introduce some of our studies. For more details, please refer to the papers. Plan4MC We study building a multi-task agent in Minecraft. Without human demonstrations, solving long-horizon tasks in this open-ended environment with reinforcement learning (RL) is extremely sample inefficient. [Read More…]
RL/Multi-Agent RL
Multi-Agent Reinforcement Learning (MARL) has recently attracted much attention from the communities of machine learning, artificial intelligence, and multi-agent systems. As an interdisciplinary research field, there are so many unsolved problems, from cooperation to competition, from agent communication to agent modeling, from centralized learning to decentralized learning. MARL has been the main research focus of our lab. We are investigating the field from many perspectives. In the following, we introduce some of our studies. [Read More…]