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.
Decision mamba: A multi-grained state space model with self-evolution regularization for offline rl.Advances in neural information processing systems, 37:22827–22849, 2024
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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.