Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning
Summary: arXiv:2604.13504v1 Announce Type: cross
Abstract
Designing effective reward functions is a cornerstone of reinforcement learning (RL), yet it remains a challenging and labor-intensive process due to the inefficiencies and inconsistencies inherent in traditional methods. Existing methods often rely on extensive manual design and evaluation steps, which are prone to redundancy and overlook local uncertainties at intermediate decision points.
To address these challenges, we propose the Chain of Uncertain Rewards (CoUR), a novel framework that integrates large language models (LLMs) to streamline reward function design and evaluation in RL environments.
Overview of CoUR
The CoUR framework introduces code uncertainty quantification with a similarity selection mechanism that combines textual and semantic analyses. This innovative approach identifies and reuses the most relevant reward function components, thereby creating a more efficient process for reward function design.
Key Features of CoUR
- Reduction of Redundant Evaluations: By leveraging the capabilities of large language models, CoUR significantly minimizes redundant evaluations of reward functions, making the process faster and more efficient.
- Bayesian Optimization: CoUR utilizes Bayesian optimization techniques on decoupled reward terms, allowing for a more robust search for optimal reward feedback.
- Integration of Textual and Semantic Analyses: The combination of these analyses enables CoUR to effectively identify and reuse components of reward functions that have proven successful in similar contexts.
Experimental Evaluation
To validate the effectiveness of the CoUR framework, we conducted comprehensive evaluations across nine original environments from IsaacGym and all 20 tasks from the Bidexterous Manipulation benchmark. The results of these experiments highlighted several significant findings:
- Enhanced Performance: CoUR demonstrated superior performance compared to traditional reward function design methods, achieving higher success rates in various RL tasks.
- Cost-Effective Evaluations: The implementation of CoUR led to a significant reduction in the cost and time associated with reward evaluations, streamlining the overall RL process.
- Robustness Across Environments: CoUR’s ability to adapt and perform well across diverse environments underscores its versatility and potential for widespread application in the field of RL.
Conclusion
The Chain of Uncertain Rewards framework represents a significant advancement in the design and evaluation of reward functions in reinforcement learning. By addressing the inefficiencies of traditional methods and integrating large language models, CoUR not only enhances performance but also reduces the costs associated with reward evaluations. As the field of reinforcement learning continues to evolve, frameworks like CoUR will play a crucial role in shaping the future of AI and machine learning applications.