Summary of Reinforcement Learning with a Focus on Adjusting Policies to Reach Targets, by Akane Tsuboya et al.
Reinforcement Learning with a Focus on Adjusting Policies to Reach Targets
by Akane Tsuboya, Yu Kono, Tatsuji Takahashi
First submitted to arxiv on: 23 Dec 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 The proposed deep reinforcement learning method prioritizes achieving an aspiration level over maximizing expected return, allowing for flexible adjustment of exploration based on target achievement proportion. This approach demonstrates improved returns in motion control and navigation tasks, while also showing potential to adapt to non-stationary environments. The findings suggest that this method may enhance exploration efficiency in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way for computers to learn from trying different actions. Normally, these agents try to get the best rewards possible, but this often takes a long time and doesn’t always work well. The new approach focuses on achieving specific goals rather than just getting high scores. By doing so, it adjusts how much it tries new things based on its progress towards those goals. In tests, this method did as well or better than other methods in controlling robotic arms and navigating through mazes. This could lead to more efficient learning for computers in real-world applications. |
Keywords
» Artificial intelligence » Reinforcement learning
