5 papers
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
Xiaojie Xu, Zongyuan Li, Chang Lu +10
StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address…
Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games
Runnan Qi, Yanan Ni, Lumin Jiang +3
This paper proposes Memory-Augmented State Machine Prompting (MASMP), a novel framework for LLM agents in real-time strategy games. Addressing key challenges like hallucinations an…
Retrieval Augmented Learning: A Retrial-based Large Language Model Self-Supervised Learning and Autonomous Knowledge Generation
Zongyuan Li, Pengfei Li, Runnan Qi +6
The lack of domain-specific data in the pre-training of Large Language Models (LLMs) severely limits LLM-based decision systems in specialized applications, while post-training a m…
LLM-PySC2: Starcraft II learning environment for Large Language Models
Zongyuan Li, Yanan Ni, Runnan Qi +12
The tremendous potential has been demonstrated by large language models (LLMs) in intelligent decision-making problems, with unprecedented capabilities shown across diverse applica…
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time
Zongyuan Li, Chang Lu, Xiaojie Xu +8
Since the emergence of the Large Language Model (LLM), LLM has been widely used in fields such as writing, translating, and searching. However, there is still great potential for L…