world2vec learns world models without rewards: an order of magnitude fewer parameters, and systems read out across 71 attractors
world2vec learns, from observations and actions alone and without reward information, an encoder that maps physical state into latent coordinates plus latent dynamics describing how that state evolves. It requires the encoded future to match what rolling out the latent dynamics predicts, a condition computable from observations and actions alone. On Pendulum, Reacher and Cartpole it performs pixel-based continuous control with at least an order of magnitude fewer parameters than DreamerV3, TD-MPC2 and LeWM, and identifies systems across a benchmark of 71 attractors.