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I have have been part of a project where we implemented Amazon Forecast service in production. As per my practical experience, we need not worry about stationarity while applying DeepAR. As per my understanding as in ARIMA the prediction function is a function around the time series moving average, but in DeepAR we are more relying on the backtest window and ...

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Both in DQN and in DDQN, the target network starts as an exact copy of Q-network, that is has the same weights, layers, input_dim, output_dim etc. as the Q-network. The main idea of the DQN agent is that the Q-network predicts the Q-values of actions from a given state and selects the maximum of them and uses the MSE Loss as its cost function. That is, it ...

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In DQN that was presented in the original paper the update target for the Q-Network is $\left(r_t + \max_aQ(s_{t+1},a;\theta^-) - Q(s_t,a_t; \theta)\right)^2$ were $\theta^-$ is some old version of the parameters that gets updated every $C$ updates, and the Q-Network with these parameters is the target network. If you didn't use this target network, i.e. if ...

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