Breaking the deadly triad with a target network
The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for...
| Main Authors: | , , |
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| Formato: | Conference item |
| Idioma: | English |
| Publicado: |
PMLR
2021
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