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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 - enter image description here 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.

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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 $p(s'| s, a)$.

In this paper that I read recently they note that learning to predict environment dynamics when the state/action space is high dimensional, as is the case with images, is difficult; so whilst it may be possible in theory it would be difficult to do and if you were predicting many steps into the future then the error would compound.

A way around this, as is done in the referenced paper, is to use predict environment dynamics in a latent space. This means that they use a latent variable to predict the next state using e.g. Variational Autoencoders.

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