Summary of Offline Reinforcement Learning with Imputed Rewards, by Carlo Romeo and Andrew D. Bagdanov
Offline Reinforcement Learning with Imputed Rewards
by Carlo Romeo, Andrew D. Bagdanov
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Reward Model in this paper addresses the challenge of limited demonstrations in Offline Reinforcement Learning (ORL) by estimating the reward signal from a small sample of environment transitions annotated with rewards. This enables the application of ORL techniques, which would otherwise be difficult or impossible to apply in data-scarce scenarios. The authors demonstrate the effectiveness of their approach on several D4RL continuous locomotion tasks, achieving high performance using only 1% of reward-labeled transitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline Reinforcement Learning is a way for computers to learn how to make decisions without interacting with the world all the time. This is helpful when it would be too expensive or difficult to simulate everything that could happen. The problem is that this type of learning usually needs a lot of examples, like videos, to teach the computer what to do. But sometimes there aren’t enough examples, and that makes it hard to use Offline Reinforcement Learning. To solve this, the researchers created a simple way to estimate how well the computer did based on just a few good examples. This allows them to use more of the data they have, even if most of it doesn’t have rewards attached. They tested their idea with some robots and showed that it works well. |
Keywords
* Artificial intelligence * Reinforcement learning
