Summary of Augmenting Offline Rl with Unlabeled Data, by Zhao Wang et al.
Augmenting Offline RL with Unlabeled Data
by Zhao Wang, Briti Gangopadhyay, Jia-Fong Yeh, Shingo Takamatsu
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces a novel approach to tackle the Out-of-Distribution (OOD) issue in offline Reinforcement Learning (Offline RL). The current state-of-the-art methods focus on conservative policy updates, adding behavior regularization or modifying the critic learning objective. However, these approaches assume that the absence of an action or state from a dataset implies its suboptimality. The authors challenge this notion and propose an offline RL teacher-student framework, complemented by a policy similarity measure. This framework enables the student policy to learn not only from the offline RL dataset but also from the knowledge transferred by a teacher policy. The teacher policy is trained using another dataset consisting of state-action pairs, which can be viewed as practical domain knowledge acquired without direct interaction with the environment. The authors believe that this additional knowledge is key to effectively solving the OOD issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help machines learn from incomplete data by combining two types of knowledge: what’s in the dataset and what experts know. When machines try to make decisions based on incomplete data, they often struggle with things that are not present in the data. The authors propose a new approach called teacher-student learning, where the student policy learns from both the data and the expertise of another policy trained on a separate dataset. This allows the student policy to learn more effectively and accurately. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning
