Reinforcement Learning (RL) has shown remarkable abilities in learning
policies for decision-making tasks. However, RL is often hindered by issues
such as low sample efficiency, lack of interpretability, and sparse supervision
signals. To tackle these limitations, we take inspiration from the human
learning process and introduce Natural Language Reinforcement Learning (NLRL),
which innovatively combines RL principles with natural language representation.
Specifically, NLRL redefines RL concepts like task objectives, policy, value
function, Bellman equation, and policy iteration in natural language space. We
present how NLRL can be practically implemented with the latest advancements in
large language models (LLMs) like GPT-4. Initial experiments over tabular MDPs
demonstrate the effectiveness, efficiency, and also interpretability of the
NLRL framework.

强化学习和自然语言表示相结合的自然语言强化学习 (NLRL) 框架在解决样本效率低、解释性差、稀疏监督信号等问题方面表现出了显著的能力。通过在自然语言空间重新定义强化学习的概念和原则，以及结合大型语言模型的先进技术如 GPT-4 的实现方式，NLRL 在标签驱动的 MDPs 上的初步实验证明了该框架的有效性、效率以及可解释性。