Summary of Understanding and Diagnosing Deep Reinforcement Learning, by Ezgi Korkmaz
Understanding and Diagnosing Deep Reinforcement Learning
by Ezgi Korkmaz
First submitted to arxiv on: 23 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 Deep neural networks have been applied to various fields, including biotechnology and finance, but their value function approximation raises concerns about decision boundary stability. The sensitivity of policy decisions to non-robust features due to complex manifolds is a limitation. To address this, we introduce a method for analyzing unstable directions in deep neural policies across time and space. Our experiments in the Arcade Learning Environment demonstrate the effectiveness of our technique in identifying correlated instability directions and measuring sample shifts’ impact on sensitive directions. State-of-the-art robust training techniques learn disjoint unstable directions with larger oscillations over time compared to standard training. This research reveals fundamental properties of decision-making processes made by reinforcement learning policies, contributing to reliable and robust deep neural policy construction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is used in many areas, but it has some limitations. When a computer makes decisions based on this type of learning, small changes can have big effects. To understand these effects, we developed a new way to analyze the decision-making process. We tested our method using video games and found that it works well. Most importantly, we learned that even the best training methods can produce unpredictable results if not used correctly. This research helps us build more reliable computer systems. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
