Summary of Causal State Distillation For Explainable Reinforcement Learning, by Wenhao Lu et al.
Causal State Distillation for Explainable Reinforcement Learning
by Wenhao Lu, Xufeng Zhao, Thilo Fryen, Jae Hee Lee, Mengdi Li, Sven Magg, Stefan Wermter
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME)
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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 The paper presents an extension to reward decomposition (RD), a technique for providing transparency in reinforcement learning (RL) models. The current RD method only provides insights based on sub-rewards, whereas this new approach delves into the cause-and-effect relationships within the RL agent’s neural model. The framework uses information-theoretic measures and leverages causal learning principles to generate local explanations that are sparse, orthogonal, and sufficient for understanding an agent’s decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making machines learn better! It’s trying to figure out why they make certain decisions when playing games or doing tasks. Right now, it’s hard to understand what these machines are thinking because their training methods don’t show us how they came up with those decisions. The idea is to break down the rewards that motivate the machine and find out which parts are most important for its actions. This new approach takes it a step further by looking at the causes and effects within the machine’s brain, or neural network. It wants to make machines more transparent so we can better understand how they work. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning
