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Benchmarking Perturbation-Based Saliency Maps for Explaining Atari Agents
One of the most prominent methods for explaining the behavior of Deep Reinforcement Learning (DRL) agents is the generation of saliency maps that show how much each pixel attributed to the agents' decision. However, there is no work that computationally evaluates and compares the fidelity of di...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326049/ https://www.ncbi.nlm.nih.gov/pubmed/35910188 http://dx.doi.org/10.3389/frai.2022.903875 |