Summary of Human-readable Programs As Actors Of Reinforcement Learning Agents Using Critic-moderated Evolution, by Senne Deproost et al.
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution
by Senne Deproost, Denis Steckelmacher, Ann Nowé
First submitted to arxiv on: 29 Oct 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 In this study, researchers investigate the transparency and explainability of Deep Reinforcement Learning (DRL) models used for controlling real-world systems. They propose Programmatic Reinforcement Learning (PRL), which creates a representation of the neural network as source code, enhancing explainability and allowing users to adapt controllers. PRL methods focus on distilling black-box policies into programs using Mean Squared Error, but this approach discards other RL algorithm components, potentially leading to poor performance compared to the original learned policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well we understand what our AI systems are doing when they’re controlling real-world things. The problem is that these “deep reinforcement learning” models can be hard to figure out because they use secret math. The researchers came up with a way to make it more transparent by turning the math into code that people can read and change. This helps us understand how our AI systems are making decisions, but it might not always work as well as the original system did. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning
