Summary of Unveiling the Decision-making Process in Reinforcement Learning with Genetic Programming, by Manuel Eberhardinger et al.
Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming
by Manuel Eberhardinger, Florian Rupp, Johannes Maucher, Setareh Maghsudi
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 proposed genetic programming framework generates explanations for the decision-making process of already trained agents by imitating them with interpretable programs. This approach can be executed to provide insights into why an agent chooses a particular action, crucial for real-world applications of deep reinforcement learning where unpredictable actions can harm individuals. The framework’s performance is comparable to the state-of-the-art methods while requiring less hardware resources and computation time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of incomprehensible predictions in machine learning and deep learning by creating explanations for agent decisions using a genetic programming framework. This framework generates interpretable programs that mimic an agent’s decision-making process, allowing us to understand why it chose a particular action. The approach is more efficient than current methods, requiring less hardware resources and computation time. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Reinforcement learning
