Summary of Reinforcement Learning and Machine Ethics:a Systematic Review, by Ajay Vishwanath and Louise A. Dennis and Marija Slavkovik
Reinforcement Learning and Machine ethics:a systematic review
by Ajay Vishwanath, Louise A. Dennis, Marija Slavkovik
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a systematic review of reinforcement learning for machine ethics and machine ethics within reinforcement learning. The authors highlight trends in ethics specifications, components, and frameworks used in reinforcement learning to achieve ethical behavior. This comprehensive review aims to consolidate the work in machine ethics and reinforcement learning, filling a gap in the state of the art landscape. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines can behave ethically. There’s already some research on this topic, but most of it focuses on other approaches rather than using special types of agents called reinforcement learning agents. The authors review all the relevant studies from the last few years that use these agents to achieve ethical behavior. They also look at what kinds of environments are used to test if the machines behave ethically. |
Keywords
» Artificial intelligence » Reinforcement learning
