Summary of Q-distribution Guided Q-learning For Offline Reinforcement Learning: Uncertainty Penalized Q-value Via Consistency Model, by Jing Zhang et al.
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
by Jing Zhang, Linjiajie Fang, Kexin Shi, Wenjia Wang, Bing-Yi Jing
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper tackles the issue of “distribution shift” in offline reinforcement learning, where a learning policy may take actions beyond the behavior policy’s knowledge, referred to as Out-of-Distribution (OOD) actions. The authors propose Q-Distribution Guided Q-Learning (QDQ), which applies a pessimistic adjustment to Q-values in OOD regions based on uncertainty estimation. This approach uses an uncertainty measure relying on the conditional Q-value distribution, learned through a high-fidelity and efficient consistency model. Additionally, the paper introduces an uncertainty-aware optimization objective for updating the Q-value function. The proposed QDQ demonstrates strong performance on the D4RL benchmark and achieves significant improvements across many tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning faces a major challenge called “distribution shift”. This happens when a policy takes actions it wasn’t trained for, making its decisions biased. To fix this, researchers use pessimistic adjustments to avoid overestimating Q-values. The authors of this paper suggest a new way to do this by penalizing Q-values in areas where the policy is unsure. They also use a special model that helps them learn about uncertainty and make better decisions. This approach works well on a test called D4RL and improves performance on many tasks. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
