Summary of Read to Play (r2-play): Decision Transformer with Multimodal Game Instruction, by Yonggang Jin et al.
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
by Yonggang Jin, Ge Zhang, Hao Zhao, Tianyu Zheng, Jarvi Guo, Liuyu Xiang, Shawn Yue, Stephen W. Huang, Zhaofeng He, Jie Fu
First submitted to arxiv on: 6 Feb 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 aims to develop a generalist agent in artificial intelligence, capable of learning multiple tasks simultaneously within Reinforcement Learning (RL). Previous studies have achieved remarkable performance using extensive offline datasets from various tasks. However, they face challenges when extending their capabilities to new tasks. The authors propose enhanced forms of task guidance to enable agents to comprehend gameplay instructions, facilitating a “read-to-play” capability. By drawing inspiration from multimodal instruction tuning in visual tasks, the study constructs a set of multimodal game instructions and incorporates them into a decision transformer. Experimental results demonstrate that this approach significantly enhances the agent’s multitasking and generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about creating a super smart computer program that can learn many things at once. Right now, we have programs that are very good at certain tasks, but they struggle when we ask them to do something new. The scientists want to make a program that can understand instructions and use that understanding to help it learn new skills. They’re trying a new approach by combining different types of information, like pictures and words, to give the program better guidance. This will help the program learn faster and be more helpful in many situations. |
Keywords
* Artificial intelligence * Generalization * Instruction tuning * Reinforcement learning * Transformer
