Feb. 14, 2024, 8:28 p.m. | /u/FastestGPU

Machine Learning www.reddit.com

**arXiv**: [https://arxiv.org/abs/2402.07157](https://arxiv.org/abs/2402.07157)

**OpenReview**: [https://openreview.net/forum?id=0VzU2H13qj](https://openreview.net/forum?id=0VzU2H13qj)


>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 …

abstract decision efficiency human inspiration interpretability language limitations low machinelearning making natural natural language process reinforcement reinforcement learning sample supervision tasks

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