Summary of Symbolic State Partitioning For Reinforcement Learning, by Mohsen Ghaffari et al.
Symbolic State Partitioning for Reinforcement Learning
by Mohsen Ghaffari, Mahsa Varshosaz, Einar Broch Johnsen, Andrzej Wąsowski
First submitted to arxiv on: 25 Sep 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 A novel approach in tabular reinforcement learning enables machines to operate directly on continuous state spaces by partitioning the state space. This partitioning improves generalization during learning, facilitating more efficient exploitation of prior experiences. As a result, the learning process accelerates and yields more reliable policies. However, traditional methods introduce approximation, which can be detrimental when dealing with nonlinear state relationships. The ideal partition should strike a balance between coarseness and capturing the key structure of the state space for the given problem. This study proposes symbolic execution to extract partitions from environment dynamics, demonstrating improved state space coverage relative to environmental behavior and superior reinforcement learning performance for sparse rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is like teaching an AI to make good decisions. Currently, these machines can’t directly learn on complex situations. To fix this, researchers created a way to divide the situation into smaller parts. This helps the AI learn faster and make better choices. However, this method isn’t perfect and might not work well with some types of situations. Scientists found a new approach that uses special computer techniques to create these divisions in a way that works better. They tested it and found it helps the AI learn faster and make more accurate decisions. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
