Summary of Guide: Real-time Human-shaped Agents, by Lingyu Zhang et al.
GUIDE: Real-Time Human-Shaped Agents
by Lingyu Zhang, Zhengran Ji, Nicholas R Waytowich, Boyuan Chen
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 A machine learning framework called GUIDE is introduced, which enables real-time human-guided reinforcement learning for improving policy learning and decision-making speed. The framework leverages continuous human feedback, grounding it into dense rewards to accelerate the learning process. A simulated feedback module learns and replicates human feedback patterns online, reducing the need for human input while allowing continual training. The performance of GUIDE is demonstrated on challenging tasks with sparse rewards and visual observations, showcasing a 30% increase in success rate compared to its RL baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning framework called GUIDE helps machines learn faster and make better decisions by getting feedback from humans. Right now, AI models can only get better with lots of data and computing power. But sometimes, we need them to make decisions quickly, even if there’s not much information available. This is where human guidance comes in. GUIDE lets humans give feedback to the machines while they’re learning, which helps them learn faster and make better choices. The framework also has a special module that learns from human feedback and can replicate it without needing more input. This means AI models can keep getting smarter over time, even with minimal human involvement. |
Keywords
» Artificial intelligence » Grounding » Machine learning » Reinforcement learning
