Keywords: Deep Offline Reinforcement Learning, Prompt Engineering, Pre-Trained Language Models
TL;DR: Prompt engineering can be successfully used for deep offline reinforcement learning in environments that are not naturally suited for the textual representation.
Abstract: In this preliminary study, we introduce a simple way to leverage pre-trained language models in deep offline RL settings that are not naturally suited for textual representation. We propose using a state transformation into a human-readable text and a minimal fine-tuning of the pre-trained language model when training with deep offline RL algorithms. This approach shows consistent performance gains on the NeoRL MuJoCo datasets. Our experiments suggest that LM fine-tuning is crucial for good performance on robotics tasks. However, we also show that it is not necessary when working with finance environments in order to retain significant improvement in the final performance.