Summary of Using Part-based Representations For Explainable Deep Reinforcement Learning, by Manos Kirtas et al.
Using Part-based Representations for Explainable Deep Reinforcement Learning
by Manos Kirtas, Konstantinos Tsampazis, Loukia Avramelou, Nikolaos Passalis, Anastasios Tefas
First submitted to arxiv on: 21 Aug 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 This paper proposes a novel approach for actor models in Deep Reinforcement Learning (RL) that enables part-based representations and adheres to non-negative constraints, enhancing interpretability. The method employs a non-negative initialization technique and a modified sign-preserving training method, which improves gradient flow compared to existing approaches. The proposed approach is demonstrated on the Cartpole benchmark, showcasing its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds new ways to use deep learning models in Reinforcement Learning. Currently, these models are hard to understand because they’re complex and have many parts. To fix this, researchers propose a way to train actor models so that they learn part-based representations while staying non-negative. This is important because it makes the model easier to understand and analyze. The new approach uses special initialization and training methods to make sure the model works well. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
