<p>Whenever you are doing reinforcement learning you periodically update the target network based on the weights of the Q network. While I do understand this helps create a stable target I do not understand how it helps the Q networks accuracy. If I am updating the Q network much more frequently than I am the target network then wont the Q network converge to and estimate of the target network that is inaccurate since the target network is being updated much slower ?</p>

CC BY-SA 4.0
2023-07-02T13:50:27.777Z
I think this https://ai.stackexchange.com/a/13459/69468 already answers your question
Luca Anzalone
CC BY-SA 4.0
2023-07-02T16:59:07.557Z
oh wow perfect this is exactly what I needed
Stef
<reinforcement-learning><deep-learning><deep-rl><dqn><deep-neural-networks>
In DQN how does the Q network not converge to the incorrect target?
Stef
# In DQN how does the Q network not converge to the incorrect target?

(Asked by: username_0 on 2023-07-01)

Whenever you are doing reinforcement learning you periodically update the target network based on the weights of the Q network. While I do understand this helps create a stable target I do not understand how it helps the Q networks accuracy. If I am updating the Q network much more frequently than I am the target network then wont the Q network converge to and estimate of the target network that is inaccurate since the target network is being updated much slower ?

**Tags:** reinforcement-learning, deep-learning, deep-rl, dqn, deep-neural-networks

> **Comments:**
> **username_1**: I think this https://ai.stackexchange.com/a/13459/69468 already answers your question  
> **username_0**: oh wow perfect this is exactly what I needed  

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## 0 answers

