Summary of State-novelty Guided Action Persistence in Deep Reinforcement Learning, by Jianshu Hu et al.
State-Novelty Guided Action Persistence in Deep Reinforcement Learning
by Jianshu Hu, Paul Weng, Yutong Ban
First submitted to arxiv on: 9 Sep 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 The paper proposes a novel approach to improving sample efficiency in deep reinforcement learning (DRL) by dynamically adjusting action persistence based on the current exploration status of the state space. The authors’ method does not require training additional value functions or policies and can be integrated with various basic exploration strategies to incorporate temporal persistence. Extensive experiments on DMControl tasks demonstrate significant improvements in sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps DRL become more efficient by using a new way to decide when to repeat actions. Instead of following a fixed rule or learning extra rules, the method adjusts its repetition strategy based on how well it’s exploring the state space. This makes it better at balancing exploration and exploitation. The authors tested their approach on various tasks and found it significantly improves efficiency. |
Keywords
» Artificial intelligence » Reinforcement learning
