Summary of An Empirical Study on the Power Of Future Prediction in Partially Observable Environments, by Jeongyeol Kwon et al.
An Empirical Study on the Power of Future Prediction in Partially Observable Environments
by Jeongyeol Kwon, Liu Yang, Robert Nowak, Josiah Hanna
First submitted to arxiv on: 11 Feb 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 This study investigates how to learn good representations of historical contexts in reinforcement learning (RL) tasks with partial observability. Researchers have found that self-predictive auxiliary tasks improve performance in fully observed settings, but their role in partial observability is still underexplored. The authors examine the effectiveness of a technique called future prediction, which involves predicting next-step observations as an auxiliary task to learn history representations. They test the hypothesis that future prediction alone can produce strong RL performance and introduce an approach called ^2 to decouple representation learning from reinforcement learning. Results show that this approach improves RL performance across multiple benchmarks requiring long-term memory, suggesting that future prediction performance serves as a reliable indicator of representation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how machines can learn about the past in situations where they don’t have all the information. Researchers already know that giving machines extra tasks to do can help them make better decisions, but what if these tasks are specifically designed to help machines remember the past? The authors tested this idea by having machines predict what would happen next as an extra task, and found that it helped them make better decisions in situations where they didn’t have all the information. This is important because it could help machines learn from experiences even when they’re not able to see everything that’s happening. |
Keywords
* Artificial intelligence * Reinforcement learning * Representation learning
