Summary of Auxiliary Reward Generation with Transition Distance Representation Learning, by Siyuan Li and Shijie Han and Yingnan Zhao and By Liang and Peng Liu
Auxiliary Reward Generation with Transition Distance Representation Learning
by Siyuan Li, Shijie Han, Yingnan Zhao, By Liang, Peng Liu
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed novel representation learning approach measures the “transition distance” between states, enabling automatic auxiliary reward generation for reinforcement learning (RL) tasks. This technique, applicable to both single-task and skill-chaining scenarios, eliminates the need for human knowledge and laborious tuning. The effectiveness of this approach is demonstrated through experiments in a wide range of manipulation tasks, resulting in improved learning efficiency and convergent stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help machines learn from experience. They’ve created a system that can automatically give rewards or penalties based on how different two states are. This is useful for robots or other machines that need to make decisions about what actions to take next. The system was tested with many different tasks and showed that it helped the machines learn more efficiently and make better choices. |
Keywords
* Artificial intelligence * Reinforcement learning * Representation learning
