From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards
This addresses exploration challenges in autonomous agents, though it appears incremental as it builds on existing RL and LLM approaches with a specific enhancement.
The paper tackles the problem of inefficient exploration in autonomous agents by extracting experience from interaction records to model game environment laws, using these as internal motivation to guide agents. The method improves overall performance for both RL and LLM agents in the Crafter environment.
Large Language Models (LLMs) and Reinforcement Learning (RL) are two powerful approaches for building autonomous agents. However, due to limited understanding of the game environment, agents often resort to inefficient exploration and trial-and-error, struggling to develop long-term strategies or make decisions. We propose a method that extracts experience from interaction records to model the underlying laws of the game environment, using these experience as internal motivation to guide agents. These experience, expressed in language, are highly flexible and can either assist agents in reasoning directly or be transformed into rewards for guiding training. Our evaluation results in Crafter demonstrate that both RL and LLM agents benefit from these experience, leading to improved overall performance.