Summary of Multi-type Preference Learning: Empowering Preference-based Reinforcement Learning with Equal Preferences, by Ziang Liu et al.
Multi-Type Preference Learning: Empowering Preference-Based Reinforcement Learning with Equal Preferences
by Ziang Liu, Junjie Xu, Xingjiao Wu, Jing Yang, Liang He
First submitted to arxiv on: 11 Sep 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 introduces a novel preference-based reinforcement learning (PBRL) method called Multi-Type Preference Learning (MTPL), which can learn from both equal and explicit preferences given by human teachers. The existing PBRL methods often focus on learning from explicit preferences, neglecting the possibility that teachers may choose equal preferences. This new task, called Equal Preference Learning Task, optimizes a neural network to promote similar reward predictions when two agents are labeled as equal preferences. By combining this task with existing methods for learning from explicit preferences, MTPL allows for simultaneous learning from both types of preferences. The proposed approach is validated through experiments applying MTPL to four state-of-the-art baselines across ten tasks in the DeepMind Control Suite. The results show that MTPL can more comprehensively understand teacher feedback and enhance feedback efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for machines to learn from people’s preferences, without needing special instructions. This is called Multi-Type Preference Learning (MTPL). Right now, most methods focus on what people explicitly like or dislike, but this method also considers when people don’t have a clear preference between two options. By combining these approaches, MTPL can better understand what people want and make decisions accordingly. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning
