Unleashing the Power of Pre-trained Language Models for Offline
  Reinforcement Learning
Authors: name: Ruizhe Shi, name: Yuyao Liu, name: Yanjie Ze, name: Simon S. Du, name: Huazhe Xu

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
Language Models for Motion Control (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
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. Our project
website is https://lamo2023.github.io