From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge
<|reference_start|>From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge: Q-shaping is an extension of Q-value initialization and serves as an alternative to reward shaping for incorporating domain knowledge to accelerate agent training, thereby improving sample efficiency by directly shaping Q-values. This approach is both general and robust across diverse tasks, allowing for immediate impact assessment while guaranteeing optimality. We evaluated Q-shaping across 20 different environments using a large language model (LLM) as the heuristic provider. The results demonstrate that Q-shaping significantly enhances sample efficiency, achieving a \textbf{16.87\%} improvement over the best baseline in each environment and a \textbf{253.80\%} improvement compared to LLM-based reward shaping methods. These findings establish Q-shaping as a superior and unbiased alternative to conventional reward shaping in reinforcement learning.<|reference_end|>
@article{wu2024from,
    title={From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with
  LLM-Guided Knowledge},
    author={Xiefeng Wu},
    journal={arXiv preprint arXiv:2410.01458},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01458},
    primaryClass={cs.AI cs.LG}
}