  The Overfitted Brain hypothesis suggests dreams happen to allow
generalization in the human brain. Here, we ask if the same is true for
reinforcement learning agents as well. Given limited experience in a real
environment, we use imagination-based reinforcement learning to train a policy
on dream-like episodes, where non-imaginative, predicted trajectories are
modified through generative augmentations. Experiments on four ProcGen
environments show that, compared to classic imagination and offline training on
collected experience, our method can reach a higher level of generalization
when dealing with sparsely rewarded environments.
