Zero-shot Model-based Reinforcement Learning using Large Language Models

2024-10-15

arXiv.org

AI Summary

This work addresses the challenge of dynamic modeling for pretrained large language models (LLMs) in continuous-state Markov decision processes (MDPs), particularly the joint modeling of multivariate time-series observations and control signals, along with uncertainty calibration. We propose Decoupled In-Context Learning (DICL), the first method enabling LLMs to perform zero-shot dynamics modeling over continuous state-action spaces. DICL employs structured decoupling to separately process observational and control inputs, and supports theoretically grounded uncertainty calibration. In model-based policy evaluation and off-policy data-augmented reinforcement learning, DICL significantly improves prediction accuracy and yields well-calibrated uncertainty estimates—without fine-tuning or domain-specific training. The implementation is open-sourced to facilitate reproducible research and community advancement.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state spaces remains understudied. In this paper, we investigate how pre-trained LLMs can be leveraged to predict in context the dynamics of continuous Markov decision processes. We identify handling multivariate data and incorporating the control signal as key challenges that limit the potential of LLMs' deployment in this setup and propose Disentangled In-Context Learning (DICL) to address them. We present proof-of-concept applications in two reinforcement learning settings: model-based policy evaluation and data-augmented off-policy reinforcement learning, supported by theoretical analysis of the proposed methods. Our experiments further demonstrate that our approach produces well-calibrated uncertainty estimates. We release the code at https://github.com/abenechehab/dicl.

Problem

• Zero-shot model-based reinforcement learning
• Continuous state spaces integration
• Disentangled In-Context Learning method

Innovation

• Leverages LLMs for continuous state spaces
• Proposes Disentangled In-Context Learning
• Enhances model-based policy evaluation