Summary of The Cross-environment Hyperparameter Setting Benchmark For Reinforcement Learning, by Andrew Patterson et al.
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
by Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
First submitted to arxiv on: 26 Jul 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 The paper introduces a new benchmark for comparing reinforcement learning (RL) algorithms across different environments using a single hyperparameter setting. This Cross-environment Hyperparameter Setting Benchmark (CHS) encourages algorithm development that is insensitive to hyperparameters and provides robust results despite statistical noise. The authors demonstrate the effectiveness of the CHS on small control environments and the DM Control suite, showing qualitatively similar results with few samples. The benchmark’s low computational cost allows for statistically sound insights at a low cost. To illustrate the applicability of the CHS to modern RL algorithms, the authors conduct an empirical study on a challenging problem in continuous control, finding no meaningful difference between Ornstein-Uhlenbeck noise and uncorrelated Gaussian noise when using the DDPG algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to compare different AI learning methods across many different situations. This helps researchers make better algorithms that work well everywhere. The method is good at avoiding mistakes caused by random chance and takes less computer power than other ways of comparing. The authors show how this works on two sets of problems, and then use it to answer a question about whether one type of noise is better for learning than another. |
Keywords
* Artificial intelligence * Hyperparameter * Reinforcement learning
