Summary of Towards a Research Community in Interpretable Reinforcement Learning: the Interppol Workshop, by Hector Kohler et al.
Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
by Hector Kohler, Quentin Delfosse, Paul Festor, Philippe Preux
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Symbolic Computation (cs.SC)
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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 abstract proposes a research direction focused on developing explainable reinforcement learning (RL) models. The authors explore the differences between explainability and interpretability in RL agents, questioning whether these concepts should be developed separately for domains that require transparency. They also investigate the advantages of interpretable policies over neural networks and discuss ways to define and measure interpretability without relying on user studies. Furthermore, they examine which reinforcement learning paradigms are best suited for developing interpretable agents and consider integrating interpretable state representations within Markov Decision Processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simpler terms, this paper is about making artificial intelligence (AI) more understandable by humans. It’s like trying to figure out how a robot learned to do something, rather than just knowing that it can. The researchers are looking for ways to make AI models more transparent and easier to understand, which could be useful in many areas where we want machines to make decisions on their own. |
Keywords
» Artificial intelligence » Reinforcement learning
