Summary of Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity Of Language Use, by Jiajun Xi et al.
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use
by Jiajun Xi, Yinong He, Jianing Yang, Yinpei Dai, Joyce Chai
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
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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 investigates how different types of language inputs can facilitate reinforcement learning (RL) in embodied agents. The study focuses on the impact of language informativeness, which includes feedback on past behaviors and future guidance, as well as language diversity, or variation of expressions, on agent learning and inference. Experimental results across four RL benchmarks show that agents trained with diverse and informative language feedback can achieve enhanced generalization and fast adaptation to new tasks. The findings highlight the critical role of language use in teaching embodied agents new tasks in an open world. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how language helps robots learn new things. Right now, most robot learning methods only use simple instructions, which isn’t like how humans talk or communicate. The researchers wanted to see if using more natural-sounding language could help robots learn faster and better. They tested different levels of language feedback and variety in their experiments and found that robots trained with this type of language performed better at learning new tasks. |
Keywords
» Artificial intelligence » Generalization » Inference » Reinforcement learning
