Summary of Natural Language Reinforcement Learning, by Xidong Feng et al.
Natural Language Reinforcement Learning
by Xidong Feng, Ziyu Wan, Haotian Fu, Bo Liu, Mengyue Yang, Girish A. Koushik, Zhiyuan Hu, Ying Wen, Jun Wang
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Natural Language Reinforcement Learning (NLRL), an innovative extension of traditional Markov Decision Process (MDP) to natural language-based representation space. NLRL redefines RL principles, including task objectives, policy, value function, Bellman equation, and policy iteration, into their language counterparts. By leveraging recent advancements in large language models (LLMs), NLRL can be practically implemented through pure prompting or gradient-based training. The paper demonstrates the effectiveness, efficiency, and interpretability of the NLRL framework on Maze, Breakthrough, and Tic-Tac-Toe games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way to make decisions using language, called Natural Language Reinforcement Learning (NLRL). It’s like playing a game where you make choices based on what you’ve learned. The researchers use special computer models to understand language and apply this understanding to make better choices. They tested NLRL on different games and showed that it works well. This breakthrough can be used in many areas, such as robotics or language translation. |
Keywords
» Artificial intelligence » Prompting » Reinforcement learning » Translation
