RL-GPT: Integrating Reinforcement Learning and Code-as-policy for Embodied Tasks in Minecraft
Основні поняття
Large Language Models (LLMs) integrated with Reinforcement Learning (RL) in RL-GPT outperform traditional methods, achieving superior efficiency in Minecraft tasks.
Анотація
RL-GPT introduces a two-level hierarchical framework to combine high-level coding and low-level RL-based actions. The framework divides tasks into sub-actions coded by LLMs and actions learned through RL. By balancing the strengths of both approaches, RL-GPT excels in challenging Minecraft tasks, demonstrating remarkable performance. The iterative process refines the coding and learning strategies, leading to optimal solutions for complex embodied tasks.
RL-GPT
Статистика
RL-GPT achieves an optimized neural network 6.7 times faster than traditional methods.
In the MineDojo environment, RL-GPT attains state-of-the-art performance on designated tasks.
RL-GPT demonstrates a 1.9x improvement over existing GPT agents.
Цитати
"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."
"Our contributions are summarized as introducing an LLMs agent utilizing an RL training pipeline as a tool."