Summary of A Method For Evaluating Hyperparameter Sensitivity in Reinforcement Learning, by Jacob Adkins et al.
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
by Jacob Adkins, Michael Bowling, Adam White
First submitted to arxiv on: 10 Dec 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 The paper proposes a new empirical methodology to study and quantify the sensitivity of reinforcement learning algorithms’ performance to hyperparameter tuning for various environments. It’s currently challenging to tune hyperparameters, as small changes can significantly impact performance, and different environments require distinct settings. The authors demonstrate their approach by analyzing the hyperparameter sensitivity of several normalization variants of PPO (Proximal Policy Optimization). Their results suggest that some performance improvements may be attributed to increased reliance on hyperparameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how machine learning algorithms work better in different situations. Right now, it’s tricky to make these algorithms perform well because small changes can have a big impact. The authors are trying to figure out why this is happening and provide a new way to study it. They tested their approach on several versions of an algorithm called PPO and found that some improvements might be due to adjusting the right settings. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization » Reinforcement learning
