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 differe...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2022-07-01
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| coleção: | Frontiers in Artificial Intelligence |
| Assuntos: | |
| Acesso em linha: | https://www.frontiersin.org/articles/10.3389/frai.2022.903875/full |
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