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About Explainability in Deep Reinforcement Learning
This document discusses explainability in deep reinforcement learning (XRL). It reviews recent works that aim to make reinforcement learning models more explainable. Explainable techniques can help users understand and trust reinforcement learning models, which are often seen as "black boxes". The document divides explainability techniques for reinforcement learning into two categories: techniques that use transparent algorithms, and post-hoc explainability techniques applied after training. It argues that explainability is important for the real-world deployment of reinforcement learning in applications like healthcare, finance, and robotics where explanations are needed for ethical and regulatory reasons.
Detailed Information
| Author: | ['Carlo Metta'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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