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Planning in latent space, rather than directly in action space, overcomes the exponential decay in feasible trajectories, unlocking efficient long-horizon decision making for embodied agents.
Normalizing Flows can now be used to train robust robotic policies in online reinforcement learning, thanks to a simple stabilization technique that overcomes previous instability issues.
