Summary of Starling: Self-supervised Training Of Text-based Reinforcement Learning Agent with Large Language Models, by Shreyas Basavatia et al.
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models
by Shreyas Basavatia, Keerthiram Murugesan, Shivam Ratnakar
First submitted to arxiv on: 9 Jun 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 The paper introduces STARLING, an interactive environment for self-supervised reinforcement learning (RL) agents in text-based games. Existing environments are domain-specific or time-consuming to generate, but STARLING bootstraps RL agents with automatically generated games to improve performance and generalization capabilities. The environment uses large language models like GPT-3 and an interactive fiction game engine based on Inform7. Experimental results show that current state-of-the-art text-based RL agents struggle to apply previously learned skills in new situations, highlighting STARLING’s potential for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special place called STARLING where machines can play text-based games to learn and get better. They use big language models and a game-making tool to make lots of games quickly. Researchers want to see if machines can really learn from these games and apply what they’ve learned in new situations. |
Keywords
» Artificial intelligence » Generalization » Gpt » Reinforcement learning » Self supervised
