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 and Planning algorithms%2C but achieving it is challenging. Inaccuracies in model estimates can compound%2C resulting in increased errors over long horizons. We approach this problem from the lens of Koopman theory%2C where the nonlinear dynamics of the environment can be linearized in a high-dimensional latent space. This allows us to efficiently parallelize the sequential problem of long-range prediction using convolution while accounting for the agent's action at every time step. Our approach also enables stability analysis and better control over gradients through time. Taken together%2C these advantages result in significant improvement over the existing approaches%2C both in the efficiency and the accuracy of modeling dynamics over extended horizons. We also show that this model can be easily incorporated into dynamics modeling for model-based planning and model-free RL and report promising experimental results.?quality=80&w=800)
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