OT-MPC computes an optimal coupling between candidate control sequences and low-cost proposals via entropy-regularized optimal transport and the Sinkhorn algorithm to improve sampling-based MPC performance.
TD-MPC2: Scalable, robust world models for continuous control
2 Pith papers cite this work. Polarity classification is still indexing.
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HaM-World integrates soft-Hamiltonian dynamics with selective state-space memory to reduce long-horizon rollout error by 55% and achieve top returns under 12 OOD perturbations on DeepMind Control Suite tasks.
citing papers explorer
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Sampling-Based Control via Entropy-Regularized Optimal Transport
OT-MPC computes an optimal coupling between candidate control sequences and low-cost proposals via entropy-regularized optimal transport and the Sinkhorn algorithm to improve sampling-based MPC performance.
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HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
HaM-World integrates soft-Hamiltonian dynamics with selective state-space memory to reduce long-horizon rollout error by 55% and achieve top returns under 12 OOD perturbations on DeepMind Control Suite tasks.