Large Language Models (LLMs) have demonstrated proficiency in utilizing
various tools by coding, yet they face limitations in handling intricate logic
and precise control. In embodied tasks, high-level planning is amenable to
direct coding, while low-level actions often necessitate task-specific
refinement, such as Reinforcement Learning (RL). To seamlessly integrate both
modalities, we introduce a two-level hierarchical framework, RL-GPT, comprising
a slow agent and a fast agent. The slow agent analyzes actions suitable for
coding, while the fast agent executes coding tasks. This decomposition
effectively focuses each agent on specific tasks, proving highly efficient
within our pipeline. Our approach outperforms traditional RL methods and
existing GPT agents, demonstrating superior efficiency. In the Minecraft game,
it rapidly obtains diamonds within a single day on an RTX3090. Additionally, it
achieves SOTA performance across all designated MineDojo tasks.

利用两级分层框架 RL-GTP，在具备高效能 coding 能力的慢速代理和执行编码任务的快速代理之间无缝融合，以高效地处理涉及复杂逻辑和精确控制的实体任务，并取得了在 Minecraft 游戏中以及指定的 MineDojo 任务上的优越表现。

RL-GPT: 集成强化学习和代码为策略

RL-GPT: Integrating Reinforcement Learning and Code-as-policy

We propose a new benchmark for planning tasks based on the Minecraft game.
Our benchmark contains 45 tasks overall, but also provides support for creating
both propositional and numeric instances of new Minecraft tasks automatically.
We benchmark numeric and propositional planning systems on these tasks, with
results demonstrating that state-of-the-art planners are currently incapable of
dealing with many of the challenges advanced by our new benchmark, such as
scaling to instances with thousands of objects. Based on these results, we
identify areas of improvement for future planners. Our framework is made
available at this https URL

我们基于 Minecraft 游戏提出了一个新的规划任务基准，其中包含 45 个任务，并且能够自动地创建新的 Minecraft 任务的命题和数值实例。我们基于这些任务对数值和命题规划系统进行基准测试，结果表明目前的先进规划器无法处理我们的新基准提出的许多挑战，例如应对具有数千个对象的实例。根据这些结果，我们确定了未来规划器的改进方向。我们的框架已经在指定的 URL 上提供。