Summary of A Survey Analyzing Generalization in Deep Reinforcement Learning, by Ezgi Korkmaz
A Survey Analyzing Generalization in Deep Reinforcement Learning
by Ezgi Korkmaz
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 research abstract focuses on deep reinforcement learning (DRL) and its ability to generalize in complex environments. DRL has been widely adopted in various fields due to its success in solving high-dimensional problems. However, the field is still grappling with the limitations of DRL’s generalization capabilities, particularly overfitting. The authors propose a comprehensive analysis of DRL’s overfitting issues and offer a range of solutions to improve policy robustness. These approaches include exploration strategies, adversarial analysis, regularization techniques, and robustness methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this study explores the limitations of using deep learning in decision-making processes. It identifies the main challenges faced by these systems when they are deployed in real-world environments with unknown dynamics. The authors provide a detailed overview of various solutions to overcome these challenges and improve the performance of these systems. This research aims to help developers create more robust and adaptable AI models that can generalize well beyond their training data. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Overfitting * Regularization * Reinforcement learning
