Beyond Human Preferences: Exploring Reinforcement Learning Trajectory Evaluation and Improvement through LLMs

TL;DR

This paper introduces LLM4PG, a framework that uses large language models to automatically generate preferences and improve reinforcement learning in complex game environments, reducing reliance on human input.

Contribution

The paper presents a novel LLM-enabled method for automatic preference generation that enhances RL performance in complex, constraint-rich environments.

Findings

LLM4PG accelerates RL convergence in complex tasks.

It overcomes stagnation caused by poor reward signals.

Reduces dependence on human preference data.

Abstract

Reinforcement learning (RL) faces challenges in evaluating policy trajectories within intricate game tasks due to the difficulty in designing comprehensive and precise reward functions. This inherent difficulty curtails the broader application of RL within game environments characterized by diverse constraints. Preference-based reinforcement learning (PbRL) presents a pioneering framework that capitalizes on human preferences as pivotal reward signals, thereby circumventing the need for meticulous reward engineering. However, obtaining preference data from human experts is costly and inefficient, especially under conditions marked by complex constraints. To tackle this challenge, we propose a LLM-enabled automatic preference generation framework named LLM4PG , which harnesses the capabilities of large language models (LLMs) to abstract trajectories, rank preferences, and reconstruct reward functions to optimize conditioned policies. Experiments on tasks with complex language constraints demonstrated the effectiveness of our LLM-enabled reward functions, accelerating RL convergence and overcoming stagnation caused by slow or absent progress under original reward structures. This approach mitigates the reliance on specialized human knowledge and demonstrates the potential of LLMs to enhance RL's effectiveness in complex environments in the wild.