Summary of Can We Hop in General? a Discussion Of Benchmark Selection and Design Using the Hopper Environment, by Claas a Voelcker et al.
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment
by Claas A Voelcker, Marcel Hussing, Eric Eaton
First submitted to arxiv on: 11 Oct 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 This paper argues that empirical benchmarking in reinforcement learning (RL) research needs to be treated as a scientific discipline, rather than relying on intuitive choices. The authors present a case study on different variants of the Hopper environment, showing how selecting standard benchmarking suites can significantly impact the evaluation of algorithm performance. They highlight a larger issue in the deep RL literature: benchmark choices are not commonly justified and lack a language to justify their selection. To address this, the paper recommends steps towards starting a dialogue about proper discussions and evaluations of benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper says that people who do research on learning machines (RL) often pick what they consider “good” examples without thinking it through. They think that’s not good enough and want to make sure that everyone agrees on how to choose these examples. They use a special test environment called Hopper as an example, showing that different choices can change the results of their tests. The paper wants people to start talking about how they choose these examples and why it matters. |
Keywords
* Artificial intelligence * Reinforcement learning
