AI engineering glossary · Reinforcement learning
What is Double DQN?
Double DQN: A change to the deep Q-network target where the online network chooses the best next action and the target network scores it. It curbs overestimation with almost no extra cost.
Double DQN: an example
Target = r + gamma * Q_target(s', argmax_a Q_online(s', a)).
Related terms
More Reinforcement learning terms
- Double Q-learning
- Dueling network
- Dyna
- Dynamic programming (reinforcement learning)
- Eligibility trace
- Entropy regularisation
- Environment
- Episode
Where you learn it
Double DQN is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.