dqn

问题 Have built a Reinforcement Learning DQN with variable length sequences as inputs, and positive and negative rewards calculated for actions. Some problem with my DQN model in Keras means that although the model runs, average rewards over time decrease, over single and multiple cycles of epsilon. This does not change even after significant period of training. My thinking is that this is due to using MeanSquareError in Keras as the Loss function (minimising error). So I am trying to implement

问题 Have built a Reinforcement Learning DQN with variable length sequences as inputs, and positive and negative rewards calculated for actions. Some problem with my DQN model in Keras means that although the model runs, average rewards over time decrease, over single and multiple cycles of epsilon. This does not change even after significant period of training. My thinking is that this is due to using MeanSquareError in Keras as the Loss function (minimising error). So I am trying to implement

问题 I have recently started learning about Deep Learning and Reinforcement Learning, and I am trying to figure out how to code a Convolutional Neural Network using Keras for a matrix of 0s and 1s with 10 rows and 3 columns. The input matrix would look like this for example [ [1, 0, 0], [0, 1, 0], [0, 0, 0], ... ] The output should be another matrix of 0s and 1s, different from the aforementioned input matrix and with a different number of rows and columns. The location of 0s and 1s in the output