Summary of Towards Sample-efficiency and Generalization Of Transfer and Inverse Reinforcement Learning: a Comprehensive Literature Review, by Hossein Hassani et al.
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
by Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Liang Lin
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 reviews the application of Transfer Learning (T-IRL) to Reinforcement Learning (RL), focusing on improving sample efficiency and generalization. RL is a type of machine learning that solves sequential decision-making problems, but it often requires large amounts of data and constructing explicit reward functions can be laborious. T-IRL methods aim to address these challenges by transferring knowledge from source domains to target domains. The paper presents fundamental T-IRL methods, reviews recent advancements in RL and T-IRL, and highlights the importance of sim-to-real strategies and human-in-the-loop approaches for efficient knowledge transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to make machine learning work better with big decisions. Right now, this type of learning takes a long time because it needs lots of practice data. The researchers are trying to find ways to speed things up by using old knowledge to learn new things. They’re also working on making sure the learning works well in different situations. This paper talks about some new ideas and approaches that might help make machine learning more useful. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Reinforcement learning » Transfer learning
