  Large Language Models (LLMs) have become integral components in various
autonomous agent systems. In this study, we present an exploration-based
trajectory optimization approach, referred to as ETO. This learning method is
designed to enhance the performance of open LLM agents. Contrary to previous
studies that exclusively train on successful expert trajectories, our method
allows agents to learn from their exploration failures. This leads to improved
performance through an iterative optimization framework. During the exploration
phase, the agent interacts with the environment while completing given tasks,
gathering failure trajectories to create contrastive trajectory pairs. In the
subsequent training phase, the agent utilizes these trajectory preference pairs
to update its policy using contrastive learning methods like DPO. This
iterative cycle of exploration and training fosters continued improvement in
the agents. Our experiments on three complex tasks demonstrate that ETO
consistently surpasses baseline performance by a large margin. Furthermore, an
examination of task-solving efficiency and potential in scenarios lacking
expert trajectory underscores the effectiveness of our approach.
