From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge

• URL: http://arxiv.org/abs/2410.01458v1
• Date: Wed, 2 Oct 2024 12:10:07 GMT
• Title: From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge
• Authors: Xiefeng Wu,
• Abstract summary: Q-shaping is an alternative to reward shaping for incorporating domain knowledge to accelerate agent training. We evaluated Q-shaping across 20 different environments using a large language model (LLM) as the provider.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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