Summary of Fine-grained Causal Dynamics Learning with Quantization For Improving Robustness in Reinforcement Learning, by Inwoo Hwang et al.
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
by Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee
First submitted to arxiv on: 5 Jun 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 novel dynamics model that infers fine-grained causal structures for enhancing robustness in reinforcement learning (RL). The key idea is to jointly learn the dynamics model with a discrete latent variable that quantizes the state-action space into subgroups. This allows the model to recognize meaningful context and sparse dependencies, leading to improved robustness in RL tasks where fine-grained causal reasoning is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to get better at making decisions based on what happens next. It’s trying to figure out why things happen together by looking at small groups of data. This helps the computer make better choices when it doesn’t know what will happen next. The new way of doing this is called “fine-grained causal reasoning”. This means that instead of just seeing big patterns, the computer can see smaller patterns within those bigger patterns. |
Keywords
» Artificial intelligence » Reinforcement learning
