IRIS (Imaginary World Models for Efficient RL) is a model-based reinforcement learning agent that learns dynamics through two components: a discrete autoencoder that compresses observations into token representations, and an autoregressive transformer that predicts future token sequences. The agent trains entirely in imagination using the learned world model, enabling sample-efficient learning. Published at ICLR 2023.