Summary of Physics-informed Model and Hybrid Planning For Efficient Dyna-style Reinforcement Learning, by Zakariae El Asri et al.
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning
by Zakariae El Asri, Olivier Sigaud, Nicolas Thome
First submitted to arxiv on: 2 Jul 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 This paper presents a reinforcement learning (RL) approach that balances asymptotic performance, sample efficiency, and inference time for real-world applications. By incorporating partial physical knowledge about system dynamics, the method learns a physics-informed model to improve sample efficiency, generates imaginary trajectories from this model to learn a model-free policy and Q-function, and combines these with learned models for efficient planning. Practical demonstrations show that the approach outperforms state-of-the-art methods in terms of compromise between sample efficiency, time efficiency, and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence (AI) to make decisions in real-world situations. Currently, AI systems are not very good at making decisions when they have to learn from experience. The researchers found a way to make these systems better by using what we know about the physical world. They created an AI model that learns quickly and makes smart decisions without needing to try everything out. This is important because it could be used in many areas like robotics, healthcare, or finance. |
Keywords
* Artificial intelligence * Inference * Reinforcement learning
