Summary of Interpretable and Editable Programmatic Tree Policies For Reinforcement Learning, by Hector Kohler et al.
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning
by Hector Kohler, Quentin Delfosse, Riad Akrour, Kristian Kersting, Philippe Preux
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep reinforcement learning agents are susceptible to goal misalignments due to their black-box nature, making it challenging to detect and correct these issues. To address this problem, we propose INTERPRETER, a fast distillation method that produces interpretable, editable tree programs for reinforcement learning. Our approach is designed to be efficient and require minimal human priors. We demonstrate the effectiveness of INTERPRETER by comparing its compact tree programs to oracle policies across various sequential decision tasks. Additionally, we evaluate the impact of our design choices on interpretability and performance. Our results show that INTERPRETER can be used to correct misalignments in Atari games and explain real-world farming strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning agents often get confused about what they’re trying to achieve. This makes it hard for us to understand why they’re making certain decisions. To solve this problem, researchers have developed a new method called INTERPRETER. It helps make the agents’ decisions more transparent and easy to change if needed. The scientists tested INTERPRETER on different tasks and found that it works well. They also showed how INTERPRETER can be used to fix mistakes in games and explain real-world farming strategies. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning
