Summary of Shire: Enhancing Sample Efficiency Using Human Intuition in Reinforcement Learning, by Amogh Joshi et al.
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning
by Amogh Joshi, Adarsh Kumar Kosta, Kaushik Roy
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO)
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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 This paper proposes a novel framework called SHIRE that encodes human intuition using Probabilistic Graphical Models (PGMs) and integrates it into the Deep Reinforcement Learning (DeepRL) training pipeline to enhance sample efficiency. The proposed approach achieves 25-78% gains in sample efficiency across various environments while maintaining negligible overhead cost. Additionally, SHIRE enhances policy explainability by teaching RL agents elementary behaviors. This framework can potentially revolutionize robotic perception and control tasks such as depth estimation, SLAM, and automatic control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep Reinforcement Learning has become a popular choice for robotics applications due to its ability to perform complex tasks without requiring large amounts of labeled data. However, it often requires a significant number of environmental interactions to converge to an acceptable solution. To address this limitation, the authors propose SHIRE, a novel framework that uses Probabilistic Graphical Models (PGMs) to encode human intuition and integrate it into the DeepRL training pipeline. This approach not only improves sample efficiency but also enhances policy explainability by teaching RL agents elementary behaviors. |
Keywords
» Artificial intelligence » Depth estimation » Reinforcement learning
