DQN for Atari takes considerable training time. For example, the 2015 paper in Nature notes that algorithms are trained for 50 million frames or equivalently around 38 days of game experience in total. One reason is that DQN for image data typically uses a CNN, which is costly to train.
However, the main purpose of a CNN is to extract the image features. Note that the policy for DQN is represented by a CNN and an output layer equal to the number of discrete actions. Is it possible to use a pretrained DQN to accelerate the training process by fixing the weights of the underlying pretrained CNN, resetting the weights of the output layer, and then running another (possibly different) DQN algorithm to relearn the weights of the output layer? Both DQN algorithms would be run on the same underlying environment.