Summary of Multi-objective Reinforcement Learning: a Tool For Pluralistic Alignment, by Peter Vamplew et al.
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment
by Peter Vamplew, Conor F Hayes, Cameron Foale, Richard Dazeley, Hadassah Harland
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper introduces a novel approach to artificial intelligence (AI) system creation through multi-objective reinforcement learning (MORL). The study highlights the limitations of traditional scalar reward-based reinforcement learning, particularly when dealing with multiple conflicting values or stakeholders. To address this issue, the authors explore MORL as an alternative, focusing on its potential in creating pluralistically-aligned AI. The paper provides a comprehensive overview of MORL’s role in achieving alignment across various perspectives and objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates artificial intelligence that can consider different opinions and goals at the same time. Right now, most AI systems are designed to follow one main goal or reward, but this approach doesn’t work well when there are multiple conflicting values or stakeholders involved. To fix this problem, scientists have developed multi-objective reinforcement learning (MORL), which uses vector rewards instead of scalar ones. The paper explains how MORL can help create AI systems that align with different perspectives and objectives. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
