HaM-World model enhances AI planning with selective memory and Hamiltonian dynamics

Researchers have introduced HaM-World, a novel structured world model designed to improve the stability and accuracy of planning in reinforcement learning. This model decomposes latent states into canonical (q, p) and context (c) subspaces, incorporating Mamba selective state-space memory to handle history-conditioned inputs. HaM-World utilizes a soft-Hamiltonian vector field for dynamics prediction, demonstrating superior performance on DeepMind Control Suite tasks and enhanced robustness against out-of-distribution perturbations. AI

影响 Introduces a more stable and robust world model for planning, potentially improving agent performance in complex and dynamic environments.

排序理由 This is a research paper detailing a new model architecture for planning in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• DeepMind Control Suite
• HaM-World
• Mamba

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