Methods › Reinforcement Learning › Q-Learning Networks › NoisyNet-DQN
NoisyNet-DQN
Introduced by Meire Fortunato et al. in Noisy Networks for Exploration
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
NoisyNet-DQN is a modification of a DQN that utilises noisy linear layers for exploration instead of ϵ-greedy exploration as in the original DQN formulation.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration 19 Jun 2020 · 0 repositories · arXiv:2006.10980
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Noisy Networks for Exploration 30 Jun 2017 · 15 repositories · arXiv:1706.10295Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Deep Reinforcement Learning | 2 |
| Reinforcement Learning | 2 |
| Reinforcement Learning (RL) | 2 |
| reinforcement-learning | 2 |
| Atari Games | 1 |
| Efficient Exploration | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections