Summary of Model-based Policy Optimization Using Symbolic World Model, by Andrey Gorodetskiy et al.
Model-based Policy Optimization using Symbolic World Model
by Andrey Gorodetskiy, Konstantin Mironov, Aleksandr Panov
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a novel approach for improving the sample efficiency of reinforcement learning algorithms in robotics, particularly when using model-free methods with low observation data quality. The authors suggest approximating transition dynamics with symbolic expressions generated via symbolic regression, which has fewer parameters than neural networks and may lead to higher accuracy and extrapolation quality. The proposed method uses a symbolic dynamics model to generate trajectories in model-based policy optimization, outperforming baseline model-free and model-based methods across various simulated tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making robots smarter by using new learning techniques. Currently, robots don’t learn very well because they need too much data. The authors came up with a way to make robots learn faster by using simple mathematical formulas instead of complicated computer programs. They tested their method on simulated tasks and found that it worked better than existing methods. |
Keywords
* Artificial intelligence * Optimization * Regression * Reinforcement learning
