Summary of Environment Descriptions For Usability and Generalisation in Reinforcement Learning, by Dennis J.n.j. Soemers and Spyridon Samothrakis and Kurt Driessens and Mark H.m. Winands
Environment Descriptions for Usability and Generalisation in Reinforcement Learning
by Dennis J.N.J. Soemers, Spyridon Samothrakis, Kurt Driessens, Mark H.M. Winands
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (stat.ML)
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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 majority of current reinforcement learning (RL) research involves training and deploying agents in environments implemented by engineers in programming languages or advanced frameworks. This paper argues that RL’s widespread adoption requires shifting focus towards methodologies where environments are described in user-friendly domain-specific or natural languages, improving usability and agent generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way for machines to learn from experience. Usually, experts create the rules for the machine to follow. But what if you could teach the machine using simple language? This idea could make it easier for people without technical expertise to use reinforcement learning. It’s like giving instructions to a robot instead of writing code. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
