Summary of Reinforcement Learning with Options and State Representation, by Ayoub Ghriss and Masashi Sugiyama and Alessandro Lazaric
Reinforcement Learning with Options and State Representation
by Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 novel approaches to enhance reinforcement learning in complex environments, focusing on Hierarchical Reinforcement Learning (HRL). By decomposing learning tasks into smaller sub-tasks, the authors aim to improve learning efficiency and effectiveness. The research leverages existing methods, such as model-based RL and curiosity-driven exploration, to develop more robust and adaptable agents. Empirical evaluations will be conducted using benchmark datasets and metrics to demonstrate the proposed approaches’ capabilities in solving complex problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a type of machine learning that helps computers learn from trial and error. In this paper, researchers want to make it easier for computers to learn when faced with very big and complicated environments. They’re trying to do this by breaking down the learning process into smaller steps, like Hierarchical Reinforcement Learning. This could help computers learn faster and better. The goal is to create more clever and flexible computer programs that can solve tricky problems. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
