Summary of Contrastive Abstraction For Reinforcement Learning, by Vihang Patil et al.
Contrastive Abstraction for Reinforcement Learning
by Vihang Patil, Markus Hofmarcher, Elisabeth Rumetshofer, Sepp Hochreiter
First submitted to arxiv on: 1 Oct 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 This paper proposes a new approach to reinforcement learning called contrastive abstraction learning (CAL), which addresses the challenge of learning in long trajectories with many states. The authors show that by reducing the number of states using abstract representations, CAL can learn more effectively. CAL consists of two phases: self-supervised contrastive learning and Hopfield network mapping. The first phase groups similar state representations together, while the second phase maps these groups to fixed points, or abstract states. This method does not require rewards and can be used for various downstream tasks. The authors demonstrate the effectiveness of CAL through experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a robot to do a task that takes many steps to complete. One way to make this easier is to group similar steps together, so the robot doesn’t have to learn everything at once. This paper shows how to do just that using something called contrastive abstraction learning (CAL). CAL makes it possible for robots to learn more effectively by reducing the number of steps they need to remember. It’s a new way of thinking about reinforcement learning that could help robots accomplish tasks on their own. |
Keywords
» Artificial intelligence » Reinforcement learning » Self supervised
