Summary of Cuer: Corrected Uniform Experience Replay For Off-policy Continuous Deep Reinforcement Learning Algorithms, by Arda Sarp Yenicesu et al.
CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms
by Arda Sarp Yenicesu, Furkan B. Mutlu, Suleyman S. Kozat, Ozgur S. Oguz
First submitted to arxiv on: 13 Jun 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 utilization of experience replay prioritization algorithms can enhance computing efficiency by reassessing the importance of transitions as they are sampled. However, these algorithms often ignore the dynamic nature of transition importance, which can negatively impact agent performance. This paper presents a novel algorithm, Corrected Uniform Experience Replay (CUER), that addresses this issue by stochastically sampling stored experiences while considering fairness among all other experiences. CUER shows promising improvements for off-policy continuous control algorithms in terms of sample efficiency, final performance, and stability of the policy during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Experience replay helps agents learn from past experiences. This technique can be improved by prioritizing important transitions as they are sampled. However, this approach can have negative effects if it doesn’t consider how the agent’s policy changes over time. A new algorithm called CUER is introduced to solve this problem. It makes sure that all stored experiences are considered fairly and equally, which leads to better performance for off-policy continuous control algorithms. |
