AI engineering glossary · Reinforcement learning
What is noisy networks?
Noisy networks: Neural networks whose weights include learned random noise, so exploration comes from the parameters instead of from random action choices such as epsilon-greedy. The amount of noise is learned and can shrink as confidence grows.
Noisy networks: an example
A noisy linear layer computes y = (mu + sigma * eps) * x with learnable mu and sigma.
Related terms
More Reinforcement learning terms
- Off-policy
- Off-policy evaluation
- Offline reinforcement learning
- On-policy
- Optimal policy
- Options framework
- Overestimation bias
- Partially observable Markov decision process
Where you learn it
Noisy networks is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.