Summary of Rethinking State Disentanglement in Causal Reinforcement Learning, by Haiyao Cao et al.
Rethinking State Disentanglement in Causal Reinforcement Learning
by Haiyao Cao, Zhen Zhang, Panpan Cai, Yuhang Liu, Jinan Zou, Ehsan Abbasnejad, Biwei Huang, Mingming Gong, Anton van den Hengel, Javen Qinfeng Shi
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach for general partially observable Markov Decision Processes (POMDPs) is proposed, which leverages insights from causality to ensure the unique recovery of latent states. By removing unnecessary assumptions and incorporating RL-specific context, the algorithm design can go beyond previous boundaries. The proposed method replaces structural constraints with two simple constraints for transition and reward preservation, guaranteeing state disentanglement faithful to underlying dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a breakthrough in reinforcement learning (RL), researchers have developed an innovative way to identify hidden states from observations when dealing with noise. This new approach uses causal analysis to ensure that the true states can be uniquely recovered. By removing assumptions and focusing on the specific RL context, the algorithm design can now go beyond previous limitations. |
Keywords
» Artificial intelligence » Reinforcement learning
