This repository provides a PyTorch implementation of DreamerV3, a reinforcement learning algorithm that learns world models to enable sample-efficient learning across diverse domains. The algorithm trains a variational autoencoder to compress observations, a transition model to predict future states, and a critic network to guide behavior. It supports benchmarking on DeepMind Control Suite, Atari 100k, Crafter, and Minecraft environments.