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A world model is a type of model that learns the dynamic laws of an environment and can predict state evolution; typical examples include Ha & Schmidhuber's World Models, the Dreamer series, and generative environment models such as Genie and GameNGen.
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10/28/2026
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VerifiedWorld models are essentially learned dynamical equations of an environment—given the current state and action, they predict the next state.78% similarUnverifiedA world model is a general term for a class of methods in reinforcement learning and robotics, whose core idea is to have the agent learn an internal simulator, with typical representatives including Dreamer, PlaNet, and Genie based on video prediction.77% similarUnverifiedA world model can be an explicit physics simulator (such as MuJoCo or Isaac Sim) or a learned implicit model (such as the recurrent state-space model used by the Dreamer series of algorithms).76% similarUnverifiedThe world model concept was systematically proposed by Ha and Schmidhuber in their 2018 paper "World Models".74% similarVerifiedA world model refers to an internal cognitive representation that enables an agent to predict the consequences of actions, perform causal reasoning, and mentally simulate situations that have not yet occurred.73% similar
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