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How to form 10 and 20 actions in corridor environment, in the paper "Dueling Network Architectures for Deep Reinforcement Learning"?

I think you are right. But then the next doubt would be why to include so many no-ops instead of just original action space+ one no-op space rather. And for this, I think it is to intentionally ...
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Does it make sense to provide a DQN with negative rewards for a network with relu and sigmoid activations?

A network with ReLU activation can predict negative values; we put ReLU between the hidden layers but return the output of the final layer without any activation function, or with a linear activation ...
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How to deal with changing rewards in Q-learning? DQN?

Is my definition of 'state' or 'action' wrong? I hesitate to say 'wrong', but that's not how state and action are defined in RL, and that mismatch might make the algorithms hard to understand. In RL ...
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Why do we use "true labels" that are based on the output of our network in Deep Q-Learning?

The labels in DQN, and in Q-learning in general, are not "true" in the sense that they represent optimal action value functions. Instead they represent approximate action values of a current ...
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