How To Implement Gradient Ascent In A Keras Dqn

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

Solution 1:

Writing a custom loss function

Here is the loss function you want

@tf.functiondefpositive_mse(y_true, y_pred):
    return -1 * tf.keras.losses.MSE(y_true, y_pred)

And then your compile line becomes

model.compile(loss=positive_mse,
          optimizer=Adam(lr=LEARNING_RATE, decay=DECAY),
          metrics=[tf.keras.losses.MeanSquaredError()])

Please note : use loss=positive_mse and not loss=positive_mse(). That's not a typo. This is because you need to pass the function, not the results of executing the function.