Summary of Building Minimal and Reusable Causal State Abstractions For Reinforcement Learning, by Zizhao Wang et al.
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
by Zizhao Wang, Caroline Wang, Xuesu Xiao, Yuke Zhu, Peter Stone
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 This paper introduces Causal Bisimulation Modeling (CBM), a method that learns task-specific state abstractions in factored state spaces for reinforcement learning. CBM leverages implicit modeling to train high-fidelity causal dynamics models, which can be reused across tasks in the same environment. The approach achieves near-oracle levels of sample efficiency and outperforms baselines on manipulation environments and the Deepmind Control Suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to help computers learn from little experience. It’s called Causal Bisimulation Modeling, or CBM for short. CBM helps computers figure out what’s important in a problem and ignore what’s not. This makes it better at learning new tasks and using the same knowledge across different problems. |
Keywords
* Artificial intelligence * Reinforcement learning
