Summary of Assessing the Impact Of Distribution Shift on Reinforcement Learning Performance, by Ted Fujimoto and Joshua Suetterlein and Samrat Chatterjee and Auroop Ganguly
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
by Ted Fujimoto, Joshua Suetterlein, Samrat Chatterjee, Auroop Ganguly
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 paper tackles the reproducibility crisis in reinforcement learning (RL) by proposing novel evaluation methods that measure the robustness of RL algorithms under distribution shifts. The authors recommend accounting for performance over time while the agent is acting in its environment, using techniques such as time series analysis. This methodology is applied to both single-agent and multi-agent environments to demonstrate the impact of introducing distribution shifts during test time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on making sure that machine learning, especially reinforcement learning, can be trusted. They’re trying to solve a problem where some results might not work well when they’re tested in different situations. To do this, they’re proposing new ways to check how well an algorithm is doing. This involves looking at what the algorithm does over time and using special tools like time series analysis. They tested these methods on simple and complex environments and found that it makes a big difference. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning * Time series
