Natural Language Reinforcement Learning
Authors: name: Xidong Feng, name: Ziyu Wan, name: Haotian Fu, name: Bo Liu, name: Mengyue Yang, name: Girish A. Koushik, name: Zhiyuan Hu, name: Ying Wen, name: Jun Wang

Reinforcement Learning (RL) mathematically formulates decision-making with
Markov Decision Process (MDP). With MDPs, researchers have achieved remarkable
breakthroughs across various domains, including games, robotics, and language
models. This paper seeks a new possibility, Natural Language Reinforcement
Learning (NLRL), by extending traditional MDP to natural language-based
representation space. Specifically, NLRL innovatively redefines RL principles,
including task objectives, policy, value function, Bellman equation, and policy
iteration, into their language counterparts. With recent advancements in large
language models (LLMs), NLRL can be practically implemented to achieve RL-like
policy and value improvement by either pure prompting or gradient-based
training. Experiments over Maze, Breakthrough, and Tic-Tac-Toe games
demonstrate the effectiveness, efficiency, and interpretability of the NLRL
framework among diverse use cases. Our code will be released at
https://github.com/waterhorse1/Natural-language-RL.