Summary of Udql: Bridging the Gap Between Mse Loss and the Optimal Value Function in Offline Reinforcement Learning, by Yu Zhang et al.
UDQL: Bridging The Gap between MSE Loss and The Optimal Value Function in Offline Reinforcement Learning
by Yu Zhang, Rui Yu, Zhipeng Yao, Wenyuan Zhang, Jun Wang, Liming Zhang
First submitted to arxiv on: 5 Jun 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 proposes a novel approach to offline reinforcement learning (RL) by addressing the overestimation phenomenon caused by the Mean Square Error (MSE) in value function estimation. The authors theoretically analyze the overestimation issue, providing an upper bound on the error, and develop a Bellman underestimated operator to counteract this problem. They then design an offline RL algorithm based on this operator and demonstrate its effectiveness on D4RL tasks, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve offline reinforcement learning by addressing a common issue with value function estimation. The Mean Square Error (MSE) is often used, but it can lead to overestimation problems. Researchers analyzed this problem and found that they could create an “underestimated” operator to help solve the issue. They then developed an algorithm using this new approach and tested it on different tasks. The results show that their method performs better than others. |
Keywords
» Artificial intelligence » Mse » Reinforcement learning
