Summary of Improving Sample Efficiency Of Reinforcement Learning with Background Knowledge From Large Language Models, by Fuxiang Zhang et al.
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models
by Fuxiang Zhang, Junyou Li, Yi-Chen Li, Zongzhang Zhang, Yang Yu, Deheng Ye
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 addresses the long-standing issue of low sample efficiency in reinforcement learning (RL) by leveraging large language models (LLMs) to impart common-sense knowledge. The authors propose a framework that extracts background knowledge about an environment, which can be applied to various downstream RL tasks. This is achieved by feeding LLMs pre-collected experiences and asking them to outline the environment’s characteristics. The extracted knowledge is then represented as potential functions for potential-based reward shaping, ensuring policy optimality from task rewards. The authors demonstrate three variants of this approach, including writing code, annotating preferences, and assigning goals, which show significant sample efficiency improvements across a range of tasks in Minigrid and Crafter domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is trying to get computers to learn by themselves by making good choices. But right now, it takes too many tries to figure out what works best. A new way to help this process uses super smart language models that can understand lots of things. The idea is to teach these models about the world and then use that knowledge to make better decisions. This paper shows how to do just that by giving the model a few experiences and asking it to describe the rules of the game. Then, we can use this information to make smart choices faster. The authors tried three different ways to do this and found that they all worked much better than before. |
Keywords
* Artificial intelligence * Reinforcement learning
