Why World Models Beat Direct Policy Learning
Most RL implementations waste samples. You run an episode, collect 1000 transitions, update your policy once, then throw away the environment interaction. DreamerV3 flips this: it learns a world model from those transitions, then trains the policy inside the dream by imagining millions of synthetic rollouts. On Atari and DMC tasks, this cuts sample complexity by 10-100x compared to model-free methods like PPO or SAC.
But here's the catch — every DreamerV3 tutorial I've found either links to the 5000-line official Jax repo or hand-waves the RSSM (Recurrent State-Space Model) as "just an RNN that models dynamics." Neither helps when you're debugging why your world model predicts garbage after 3 timesteps.
This post builds DreamerV3 from scratch in PyTorch, focusing on the three components that actually matter: the RSSM dynamics model, the actor-critic dreaming in latent space, and the KL balancing trick that keeps representations from collapsing. I'll show what breaks, what hyperparameters are sensitive, and when world models don't help.
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