Summary of Learning Uncertainty-aware Temporally-extended Actions, by Joongkyu Lee et al.
Learning Uncertainty-Aware Temporally-Extended Actions
by Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes a novel algorithm for reinforcement learning called Uncertainty-aware Temporal Extension (UTE). Traditional action repetition techniques can degrade performance when sub-optimal actions are repeated, but UTE addresses this limitation by employing ensemble methods to accurately measure uncertainty during action extension. This allows policies to strategically choose between exploration and uncertainty-averse approaches, tailored to their specific needs. The algorithm is tested in Gridworld and Atari 2600 environments, demonstrating significant improvements over existing action repetition algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better by making a new way to improve reinforcement learning called Uncertainty-aware Temporal Extension (UTE). Usually, when we repeat actions that aren’t the best, it makes things worse. But UTE can fix this problem by measuring how uncertain our actions are and then choosing what to do based on that information. This means we can explore more or play it safe, depending on what works best for us. The scientists tested UTE in some games and showed that it really does make a big difference. |
Keywords
* Artificial intelligence * Reinforcement learning
