Neural networks and differential dynamic programming for reinforcement learning problems

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Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019, neutral): “omponent in our framework. The framework can be extended to other types of environments, such as partially observable and highdimensional ones, through, e.g., probabilistic or differentiable planning [49, 94, 80], latent embedding of (belief) states [36] and goals [67]. Other directions are to adapt”
Reasoning About Liquids via Closed-Loop Simulation (2017, background): “Yamaguchi and Atkeson did, although, follow up their simulated work with pouring on a real robot [35].”