Summary of State Chrono Representation For Enhancing Generalization in Reinforcement Learning, by Jianda Chen et al.
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
by Jianda Chen, Wen Zheng Terence Ng, Zichen Chen, Sinno Jialin Pan, Tianwei Zhang
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 In a reinforcement learning context with image-based inputs, it’s essential to develop a robust and generalizable state representation. Recent advances in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representations from pixel observations. However, these approaches struggle with demanding generalization tasks and scenarios with non-informative rewards due to their inability to capture sufficient long-term information. To address this, the authors propose a novel State Chrono Representation (SCR) approach that incorporates extensive temporal information into the update step of bisimulation metric learning. SCR learns state distances within a temporal framework considering both future dynamics and cumulative rewards over current and long-term future states. The proposed strategy effectively incorporates future behavioral information without introducing additional parameters for modeling dynamics. Compared to recent metric-based methods, SCR achieves better performance in demanding generalization tasks in the DeepMind Control and Meta-World environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a paper about making machines learn from images, researchers are trying to figure out how to teach computers a good way to represent states (like where an object is or what’s happening). They’re using something called metric learning to do this. This approach has been pretty successful so far, but there are some challenges when it comes to teaching the computer to generalize and make decisions based on incomplete information. To solve this problem, the researchers created a new method called State Chrono Representation (SCR) that takes into account what might happen in the future and how rewards will change over time. They tested SCR in different environments and found that it did better than other methods at making predictions. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
