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For questions related to the concept of loss (or cost) function in the context of machine learning.
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How to stop DQN Q function from increasing during learning?
I changed the rewards to be negative and positive by substructing the mean reward.
It seems to improve the Q function boundries.
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How to stop DQN Q function from increasing during learning?
Following the DQN algorithm with experience replay:
Store transition $\left(\phi_{t}, a_{t}, r_{t}, \phi_{t+1}\right)$ in $D$ Sample random minibatch of transitions $\left(\phi_{j}, a_{j}, r_{j}, \phi …