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...
| 主要な著者: | , , |
|---|---|
| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
PMLR
2021
|