Summary of Exclusively Penalized Q-learning For Offline Reinforcement Learning, by Junghyuk Yeom et al.
Exclusively Penalized Q-learning for Offline Reinforcement Learning
by Junghyuk Yeom, Yonghyeon Jo, Jungmo Kim, Sanghyeon Lee, Seungyul Han
First submitted to arxiv on: 23 May 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 A novel offline reinforcement learning (RL) approach, Exclusively Penalized Q-learning (EPQ), is proposed to mitigate overestimation errors caused by distributional shift. Existing penalized value function-based offline RL methods are shown to have a limitation that can introduce underestimation bias, leading to suboptimal performance. EPQ addresses this concern by selectively penalizing states prone to inducing estimation errors, resulting in reduced underestimation bias and improved performance in various offline control tasks compared to other offline RL methods. The proposed method leverages constraint-based techniques to improve the accuracy of value function estimates, showcasing its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is a way to train robots or computers to make good decisions without needing to be constantly connected to the internet. Sometimes, this training can go wrong and give the robot bad ideas about what’s likely to happen next. This paper talks about how they came up with a new way to fix that problem by giving the robot better information about which things it should avoid doing. They tested their idea on some computer simulations and found that it worked much better than other methods. |
Keywords
» Artificial intelligence » Reinforcement learning
