Summary of Knowledgeable Agents by Offline Reinforcement Learning From Large Language Model Rollouts, By Jing-cheng Pang et al.
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
by Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li, Jiaji Zhang, Xiong-Hui Chen, Nan Tang, Yang Yu
First submitted to arxiv on: 14 Apr 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 introduces a novel method for training agents in complex tasks through environmental interaction data. The approach leverages knowledge from large language models (LLMs) to create knowledgeable agents. The method, called Knowledgeable Agents from Language Model Rollouts (KALM), extracts knowledge from LLMs in the form of imaginary rollouts that can be learned by the agent through offline reinforcement learning methods. KALM fine-tunes the LLM to perform various tasks based on environmental data, enabling it to generate diverse and meaningful imaginary rollouts that reflect novel skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines large language models with reinforcement learning to train agents for complex tasks. It introduces a new method called KALM (Knowledgeable Agents from Language Model Rollouts). KALM uses LLMs to create imaginary rollouts that agents can learn from. The paper shows that KALM helps agents complete complex tasks and extends their capabilities to novel tasks. This could be useful for robots or other AI systems that need to learn new skills. |
Keywords
» Artificial intelligence » Language model » Reinforcement learning
