Summary of Adaptive Advantage-guided Policy Regularization For Offline Reinforcement Learning, by Tenglong Liu et al.
Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
by Tenglong Liu, Yang Li, Yixing Lan, Hao Gao, Wei Pan, Xin Xu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 proposed Adaptive Advantage-guided Policy Regularization (A2PR) method aims to address the challenge of out-of-distribution (OOD) in offline reinforcement learning. Existing methods often constrain the learned policy through policy regularization, but these methods can suffer from unnecessary conservativeness, hampering policy improvement. A2PR combines an augmented behavior policy with a Variational Autoencoder (VAE) to guide the learned policy, selecting high-advantage actions that differ from those present in the dataset while maintaining conservatism from OOD actions. Theoretical analysis proves that A2PR improves the behavior policy and mitigates value overestimation with a bounded performance gap. Experimental results on the D4RL benchmark demonstrate state-of-the-art performance, and additional experiments on suboptimal mixed datasets reveal superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A2PR is a new way to learn policies from data that are not perfect. It’s like trying to find the best route in a map, but some parts of the map are missing or incorrect. Existing methods try to be too careful and don’t take enough risks, which can make them worse than they need to be. A2PR helps by selecting better actions based on what it knows about the data, while still being careful not to choose actions that are very unlikely. This makes it a powerful tool for learning policies from imperfect data. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning » Variational autoencoder
