Summary of Pausing Policy Learning in Non-stationary Reinforcement Learning, by Hyunin Lee et al.
Pausing Policy Learning in Non-stationary Reinforcement Learning
by Hyunin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
First submitted to arxiv on: 25 May 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 paper challenges the common belief that continually updating decision models in real-time reinforcement learning is optimal for minimizing temporal gaps. Instead, it proposes forecasting an online reinforcement learning framework and shows that strategically pausing decision updates can lead to better overall performance by effectively managing aleatoric uncertainty. The proposed framework uses a non-zero policy hold duration, which provides a sharper upper bound on dynamic regret. Experimental evaluations on three different environments demonstrate that this approach yields higher rewards compared to continuous decision updates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In real-time reinforcement learning, the system collects data from the past, updates its decision model in the present, and deploys it in the future. The paper shows that pausing decision updates can be better than continually updating them. This helps manage uncertainty and makes decisions more effective. The paper also provides a formula for finding the right balance between updating and holding policy decisions. Experiments on different environments show that this approach works well. |
Keywords
» Artificial intelligence » Reinforcement learning
