Solving long-horizon, temporally-extended tasks using Reinforcement Learning
(RL) is challenging, compounded by the common practice of learning without
prior knowledge (or tabula rasa learning). Humans can generate and execute
plans with temporally-extended actions and quickly learn to perform new tasks
because we almost never solve problems from scratch. We want autonomous agents
to have this same ability. Recently, LLMs have been shown to encode a
tremendous amount of knowledge about the world and to perform impressive
in-context learning and reasoning. However, using LLMs to solve real world
problems is hard because they are not grounded in the current task. In this
paper we exploit the planning capabilities of LLMs while using RL to provide
learning from the environment, resulting in a hierarchical agent that uses LLMs
to solve long-horizon tasks. Instead of completely relying on LLMs, they guide
a high-level policy, making learning significantly more sample efficient. This
approach is evaluated in simulation environments such as MiniGrid, SkillHack,
and Crafter, and on a real robot arm in block manipulation tasks. We show that
agents trained using our approach outperform other baselines methods and, once
trained, don't need access to LLMs during deployment.

利用强化学习解决长期和拓展性任务很具挑战性，特别是在没有先验知识的情况下，为了提高样本效率，本文通过利用 LLMs 的规划能力结合强化学习的环境学习，构建了一个层次化代理，用于解决长期任务，并在 MiniGrid、SkillHack 和 Crafter 等仿真环境以及实际机械臂的块操作任务中验证了该方法的优越性能，且训练完成后不需要依赖 LLMs 进行部署。