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Found 5 papers, 1 papers with code
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e. g., video.
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e. g., video.
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot.
