Natural Language Reinforcement Learning
🤖 AI Summary
To address core challenges in reinforcement learning—including low sample efficiency, poor interpretability, and sparse rewards—this paper proposes Natural Language Reinforcement Learning (NLRL), a novel framework that systematically maps foundational RL concepts (e.g., the Bellman equation and policy iteration) into natural language space, enabling language-based MDP modeling, policy learning, and value estimation. Methodologically, NLRL integrates large language models (GPT-4), symbolic prompt engineering, and language-grounded policy reasoning to ensure that all policy decisions are human-readable, traceable, and editable. Empirical evaluation on tabular MDP tasks demonstrates that NLRL significantly accelerates convergence, improves sample efficiency, and enhances human-agent collaboration and policy interpretability. By grounding RL semantics in natural language, NLRL establishes a new paradigm for building intelligible, transparent, and human-intervenable agents.
Application Category
📝 Abstract
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.
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