Summary of Position: Benchmarking Is Limited in Reinforcement Learning Research, by Scott M. Jordan et al.
Position: Benchmarking is Limited in Reinforcement Learning Research
by Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 A novel reinforcement learning framework is proposed to address the issue of costly and time-consuming experimental practices in the field of artificial intelligence. The current standard approach relies on evaluating performance on benchmark environments and comparing results with established algorithms. However, this method often leads to misleading or unsupported claims. The authors identify computational costs as a primary obstacle to conducting rigorous benchmarking experiments. They demonstrate that these costs can be prohibitively high, making it challenging to design and execute reliable experiments. To overcome this limitation, the paper suggests an alternative experimentation paradigm that can help reduce the burden of rigorous performance benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is needed to test artificial intelligence systems because the current method is taking too long and using too much computer power. Right now, people compare how well different AI systems do on certain tasks, but this process often leads to false or unclear results. The main problem is that testing these systems thoroughly requires a lot of computing power and time. This paper looks at why it’s so hard to test AI systems correctly and proposes a new way to do it. |
Keywords
* Artificial intelligence * Reinforcement learning
