Unleashing The Power Of Pre-trained Language Models For Offline Reinforcement Learning

Shi Ruizhe, Liu Yuyao, Ze Yanjie, Du Simon S., Xu Huazhe. Arxiv 2023

[Paper]    
Agentic Few Shot Fine Tuning Model Architecture Pretraining Methods Reinforcement Learning Tools Training Techniques Transformer

Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. In real-world scenarios, data collection could be costly and risky; therefore, offline RL becomes particularly challenging when the in-domain data is limited. Given recent advances in Large Language Models (LLMs) and their few-shot learning prowess, this paper introduces \(\textbf{La}\)nguage Models for \(\textbf{Mo}\)tion Control (\(\textbf{LaMo}\)), a general framework based on Decision Transformers to effectively use pre-trained Language Models (LMs) for offline RL. Our framework highlights four crucial components: (1) Initializing Decision Transformers with sequentially pre-trained LMs, (2) employing the LoRA fine-tuning method, in contrast to full-weight fine-tuning, to combine the pre-trained knowledge from LMs and in-domain knowledge effectively, (3) using the non-linear MLP transformation instead of linear projections, to generate embeddings, and (4) integrating an auxiliary language prediction loss during fine-tuning to stabilize the LMs and retain their original abilities on languages. Empirical results indicate \(\textbf{LaMo}\) achieves state-of-the-art performance in sparse-reward tasks and closes the gap between value-based offline RL methods and decision transformers in dense-reward tasks. In particular, our method demonstrates superior performance in scenarios with limited data samples.

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