Mental Modeling of Reinforcement Learning Agents by Language Models
The study examines large language models' (LLMs) ability to understand reinforcement learning (RL) agents through agent mental modeling. LLMs currently struggle to fully model agents, highlighting the importance of enhancing their capacity.
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The study explores the ability of large language models (LLMs) to create a mental model of reinforcement learning (RL) agents, termed agent mental modeling. It investigates how well LLMs can understand an agent's behavior and its impact on states based on interaction history. The research introduces evaluation metrics tested on various RL task datasets, revealing that LLMs currently cannot fully model agents through inference alone. The findings shed light on the potential and limitations of modern LLMs in comprehending RL agent behavior, crucial for explainable reinforcement learning (XRL). The study emphasizes the need for further innovations to enhance LLMs' capacity for mental modeling agents.
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