Summary of Unveiling the Role Of Expert Guidance: a Comparative Analysis Of User-centered Imitation Learning and Traditional Reinforcement Learning, by Amr Gomaa and Bilal Mahdy
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning
by Amr Gomaa, Bilal Mahdy
First submitted to arxiv on: 28 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 comparative study investigates the performance, robustness, and limitations of imitation learning versus traditional reinforcement learning methods in intelligent systems. The paper examines the impact of expert guidance and suboptimal demonstrations on the learning process, highlighting the value of human-in-the-loop feedback. The authors conduct extensive experimentation using the Unity platform and analyze the effectiveness and limitations of these approaches. The study contributes to the advancement of human-centered artificial intelligence by showcasing the benefits and challenges of incorporating human feedback into the learning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares two ways intelligent systems learn: imitation learning and traditional reinforcement learning. It looks at how well each method works, especially when humans give feedback or do things a little bit wrong. The study uses a special computer program called Unity to test these methods and see what works best. The results help create better artificial intelligence that can solve real-world problems. |
Keywords
* Artificial intelligence * Reinforcement learning
