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Traditional reinforcement-learning-based agents rely on sparse rewards thatoften only use binary values to indicate task completion or failure. Thechallenge in exploration efficiency makes it difficult to effectively learncomplex tasks in Minecraft. To address this, this paper introduces an advancedlearning system, named Auto MC-Reward, that leverages Large Language Models(LLMs) to automatically design dense reward functions, thereby enhancing thelearning efficiency. Auto MC-Reward consists of three important components:Reward Designer, Reward Critic, and Trajectory Analyzer. Given the environmentinformation and task descriptions, the Reward Designer first design the rewardfunction by coding an executable Python function with predefined observationinputs. Then, our Reward Critic will be responsible for verifying the code,checking whether the code is self-consistent and free of syntax and semanticerrors. Further, the Trajectory Analyzer summarizes possible failure causes andprovides refinement suggestions according to collected trajectories. In thenext round, Reward Designer will take further refine and iterate the densereward function based on feedback. Experiments demonstrate a significantimprovement in the success rate and learning efficiency of our agents incomplex tasks in Minecraft, such as obtaining diamond with the efficientability to avoid lava, and efficiently explore trees and animals that aresparse on the plains biome.

Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft