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DQN implemented at https://github.com/PacktPublishing/PyTorch-1.x-Reinforcement-Learning-Cookbook/blob/master/Chapter07/chapter7/dqn.py uses the mean square error loss function for the neural network to learn the state -> action mapping :self.criterion=torch.nn.MSELoss()Could cross-entropy be used...
Asked on 12/16/2021
0 answerIt is proved that the Bellman update is a contraction (1). Here is the Bellman update that is used for Q-Learning: $$Q_{t+1}(s, a) = Q_{t}(s, a) + alpha*(r(s, a,...
Asked on 12/16/2021 by sirfroggy
0 answerWhile reading the DQN paper, I found that randomly selecting and learning samples reduced divergence in RL using a non-linear function approximator (e.g a neural network). So,...
Asked on 12/13/2021
1 answerIn the basic variant of GCN we have the following: Here we aggregate the information from the...
Asked on 12/13/2021
1 answerIn my code, I usually use the mean squared error (MSE), but the TensorFlow tutorials always use the categorical cross-entropy (CCE). Is the CCE loss function better than MSE? Or...
Asked on 12/11/2021
3 answerI have recently encountered the paper on NLP. It is very new to me and I am still unable to see how that works. I have used all the resources...
Asked on 12/11/2021
1 answerI have been creating sports betting algorithms for many years using Microsoft access and I am transitioning to the ML world and trying to get a grasp on determining the...
Asked on 12/11/2021 by Sports_Stats
1 answerIn the proof of the policy gradient theorem in the RL book of Sutton and Barto (that I shamelessly paste here): ...
Asked on 12/09/2021
2 answerI'm wondering, has anyone seen any paper where one trains a network but biases it to produce similar outputs to a given model (such as one given from expert opinion...
Asked on 12/09/2021 by BLBA
0 answerI have used a different setting, but DDPG is not learning and it does not converge. I have used these codes 1,2, and ...
Asked on 12/09/2021 by I_Al-thamary
0 answerGet help from others!
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