# Can a convolutional network predict states for a RL Agent

During the course of training a DQN agent, all visited states are stored in a replay buffer. Therefore would it be practically possible for a CNN, given a reasonable amount of data, to predict the next RL state (in the form of an image) for a given action?

For example, take the game of Atari as shown below - Here the agent can take 2 major actions - go left and go right. Would a CNN be generate the image of the bar going right/left for the respective actions? My practical knowledge of CNNs is quite limited and therefore I'm trying to gauge the abilities of CNNs before I take up a project.

It sounds like what you're suggesting is similar to what is done in methods that use a planner. These methods looks to learn the dynamics of the MDP to use to plan during training; that is they want to be able to learn the transition probabilities $$p(s'| s, a)$$.