Summary of World Models with Hints Of Large Language Models For Goal Achieving, by Zeyuan Liu et al.
World Models with Hints of Large Language Models for Goal Achieving
by Zeyuan Liu, Ziyu Huan, Xiyao Wang, Jiafei Lyu, Jian Tao, Xiu Li, Furong Huang, Huazhe Xu
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 research proposes a new reinforcement learning approach called Dreaming with Large Language Models (DLLM) to overcome the challenges of long-horizon tasks and sparse goals in complex environments. The existing methods addressing this issue may not provide meaningful guidance, especially when dealing with large state and action spaces. DLLM integrates hinting subgoals from language models into model rollouts to encourage goal discovery and reaching. By assigning intrinsic rewards to samples aligning with the hints, DLLM guides the agent toward efficient exploration. Compared to recent methods, DLLM outperforms in various challenging environments, such as HomeGrid, Crafter, and Minecraft. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this study develops a new way for machines to learn from complex tasks by using large language models. It helps them figure out what goals are important and how to reach them, which is really useful for tasks like building or crafting in video games or virtual worlds. The new approach works better than others in similar situations. |
Keywords
» Artificial intelligence » Reinforcement learning
