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Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models

Maryam Shoaeinaeini, Brent Harrison

TL;DR

A calibrated guidance system that uses Monte Carlo Dropout to enhance LLM advice reliability by assessing prediction variances from multiple forward passes is introduced and a novel RL policy shaping method based on dynamic model average entropy is developed to adjust the LLM's influence on RL policies according to guidance uncertainty.

Abstract

Human guidance in reinforcement learning (RL) is often impractical for large-scale applications due to high costs and time constraints. Large Language Models (LLMs) offer a promising alternative to mitigate RL sample inefficiency and potentially replace human trainers. However, applying LLMs as RL trainers is challenging due to their overconfidence and less reliable solutions in sequential tasks. We address this limitation by introducing a calibrated guidance system that uses Monte Carlo Dropout to enhance LLM advice reliability by assessing prediction variances from multiple forward passes. Additionally, we develop a novel RL policy shaping method based on dynamic model average entropy to adjust the LLM's influence on RL policies according to guidance uncertainty. This approach ensures robust RL training by relying on reliable LLM guidance. To validate our contributions, we conduct extensive experiments in a Minigrid environment with three goals in varying environment sizes. The results showcase superior model performance compared to uncalibrated LLMs, unguided RL, and calibrated LLMs with different shaping policies. Moreover, we analyze various uncertainty estimation methods, demonstrating the effectiveness of average entropy in reflecting higher uncertainty in incorrect guidance. These findings highlight the persistent overconfidence in fine-tuned LLMs and underscore the importance of effective calibration in sequential decision-making problems.

Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models

TL;DR

A calibrated guidance system that uses Monte Carlo Dropout to enhance LLM advice reliability by assessing prediction variances from multiple forward passes is introduced and a novel RL policy shaping method based on dynamic model average entropy is developed to adjust the LLM's influence on RL policies according to guidance uncertainty.

Abstract

Human guidance in reinforcement learning (RL) is often impractical for large-scale applications due to high costs and time constraints. Large Language Models (LLMs) offer a promising alternative to mitigate RL sample inefficiency and potentially replace human trainers. However, applying LLMs as RL trainers is challenging due to their overconfidence and less reliable solutions in sequential tasks. We address this limitation by introducing a calibrated guidance system that uses Monte Carlo Dropout to enhance LLM advice reliability by assessing prediction variances from multiple forward passes. Additionally, we develop a novel RL policy shaping method based on dynamic model average entropy to adjust the LLM's influence on RL policies according to guidance uncertainty. This approach ensures robust RL training by relying on reliable LLM guidance. To validate our contributions, we conduct extensive experiments in a Minigrid environment with three goals in varying environment sizes. The results showcase superior model performance compared to uncalibrated LLMs, unguided RL, and calibrated LLMs with different shaping policies. Moreover, we analyze various uncertainty estimation methods, demonstrating the effectiveness of average entropy in reflecting higher uncertainty in incorrect guidance. These findings highlight the persistent overconfidence in fine-tuned LLMs and underscore the importance of effective calibration in sequential decision-making problems.

Paper Structure

This paper contains 18 sections, 3 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Reinforcement Learning
  5. Policy Shaping
  6. LLM's Customization
  7. Uncertainty Evaluation
  8. Calibrated LLM Trainer Structure
  9. Calibration System Framework
  10. LLM-Enhanced RL Architecture
  11. Experimental Setup
  12. Fine-Tuning:
  13. Model Parameters
  14. Environment and Reward Structure
  15. Prompt Engineering
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Illustraition of three types of uncertainty estimation methods.
  • Figure 2: The calibration system architecture using MC Dropout in the fine-tuned LLM.
  • Figure 3: The structure of calibrated LLM-based RL system
  • Figure 4: An instance of agent's state
  • Figure 5: Comparison of four models—uncalibrated LLM guided, unguided RL, linear policy shaping, and our uncertainty-aware policy shaping model.
  • ...and 2 more figures