The paper focuses on introducing a new method called Decision-Pretrained Transformer (DPT) that utilizes supervised pretraining to equip transformer models with the ability to make decisions in new reinforcement learning environments based on a small set of examples. It showcases how DPT can efficiently learn decision-making strategies without the need for explicit training for exploration or exploitation. Engineers and specialists can leverage the DPT methodology to design more versatile and efficient RL agents. By learning a decision-making strategy through supervised pretraining, DPT demonstrates adaptability to new environments, ability to explore and exploit, and strong generalization capabilities. This approach offers a promising path towards practical and efficient Bayesian RL methods. Read full paper: https://arxiv.org/abs/2306.14892 Tags: Reinforcement Learning, Transformer Models, Decision-Making