4 papers · 1 filter
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…
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…
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…