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Natural Language Reinforcement Learning
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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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Cited by 1 Pith paper
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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback
A survey of prior work on using human and LLM feedback to improve reinforcement learning, plus attention-based methods for large state spaces.
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