Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents
Researchers propose a mathematical framework to integrate large language models with reinforcement learning, ensuring policy stability even with imperfect LLM feedback.
- Introduces a formal framework for hybrid LLM-RL agents using Goal-Augmented Markov Decision Processes.
- Proves that LLM-derived reward shaping preserves optimal policies even with inaccurate feedback.
- Validated numerically on a small MDP, showing practical feasibility.
- Stronger theoretical guarantees compared to existing LLM-as-reward methods.
A new paper introduces a formal framework for combining large language models (LLMs) with reinforcement learning (RL) agents. The approach models the hybrid system as a Goal-Augmented Markov Decision Process, where the LLM provides per-state progress scores used as bounded potential functions. This method guarantees that the optimal policy set remains unchanged, even when the LLM-derived rewards are inaccurate. The theoretical guarantee is stronger than existing LLM-as-reward approaches, addressing a key challenge in hybrid AI systems. The framework was numerically validated on a small Markov Decision Process (MDP), demonstrating its practical feasibility. The work aims to bridge the gap between theoretical rigor and practical deployment in hybrid AI agents.
Provides a mathematically sound way to integrate LLMs with RL for more stable hybrid agents.
Could lead to more reliable AI systems in production environments.
Highlights emerging research in hybrid AI systems with potential commercial applications.
Offers a clear theoretical foundation for combining LLMs and reinforcement learning.
- Goal-Augmented Markov Decision Process
- A mathematical framework extending traditional MDPs by incorporating goal-oriented state representations.
- LLM-as-reward
- A technique where large language models provide reward signals for reinforcement learning agents.
- Potential function
- A function used in reward shaping to guide an agent toward desired states by adding auxiliary rewards.
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