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Ball-region CGSReg improves pixel-space zero-shot MBRL in DreamerV3, DIAMOND, TWISTER, and Simulus, and also improves closed-loop rollouts in the first three; STORM shows no clear improvement.
World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, leaving their standalone reliability understudied. We reproduce five visual world-model agents in Atari Pong -- DreamerV3, DIAMOND, TWISTER, Simulus, and STORM -- and match their reported agent performance. We then fr...
The Direct Prediction World Model is introduced, a non-recursive architecture that compresses an action sequence of arbitrary length into a single embedding and predicts the endpoint observation in a single forward pass, making long-horizon end-to-end training practical at horizons where unrolled autoregressive trainin...
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