Self reward design with fine-grained interpretability
The black-box nature of deep neural networks (DNN) has brought to attention the issues of transparency and fairness. Deep Reinforcement Learning (Deep RL or DRL), which uses DNN to learn its policy, value functions etc, is thus also subject to similar concerns. This paper proposes a way to circumven...
| Principais autores: | , |
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| Outros Autores: | |
| Formato: | Journal Article |
| Idioma: | English |
| Publicado em: |
2023
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| Assuntos: | |
| Acesso em linha: | https://hdl.handle.net/10356/169406 |