Summary of Model-based Transfer Learning For Contextual Reinforcement Learning, by Jung-hoon Cho et al.
Model-Based Transfer Learning for Contextual Reinforcement Learning
by Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu
First submitted to arxiv on: 8 Aug 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 A deep reinforcement learning approach is introduced that addresses brittleness by selecting training tasks strategically for maximum generalization performance across various tasks. The Model-Based Transfer Learning (MBTL) method layers on top of existing RL methods to effectively solve contextual problems. MBTL models generalization performance in two parts: the performance set point, modeled using Gaussian processes, and the performance loss (generalization gap), modeled as a linear function of contextual similarity. It combines these pieces within a Bayesian optimization framework to select training tasks. The method exhibits sublinear regret in the number of training tasks and can achieve up to 43x improved sample efficiency compared with canonical independent training and multi-task training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning to help computers make better decisions. Right now, some machines can learn by doing things over and over again, but they often get stuck if something small changes in the environment. The researchers wanted to find a way to help these machines learn more quickly and adapt to new situations. They developed a new method called Model-Based Transfer Learning (MBTL) that allows machines to learn from one task and apply what they learned to other related tasks. This method uses special algorithms to model how well the machine will do on different tasks, and then it chooses which tasks to practice on first. The results show that this method can help machines learn much faster and make better decisions. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Multi task » Optimization » Reinforcement learning » Transfer learning
