  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.
