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A probabilistic approach to blood glucose prediction in type 1 diabetes under meal uncertainties
Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes
are hybrid closed-loop systems, which require the user to announce every meal and its size …
are hybrid closed-loop systems, which require the user to announce every meal and its size …
Multi time scale world models
Intelligent agents use internal world models to reason and make predictions about different
courses of their actions at many scales. Devising learning paradigms and architectures that …
courses of their actions at many scales. Devising learning paradigms and architectures that …
Zero-shot reinforcement learning via function encoders
Although reinforcement learning (RL) can solve many challenging sequential decision
making problems, achieving zero-shot transfer across related tasks remains a challenge …
making problems, achieving zero-shot transfer across related tasks remains a challenge …
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Probabilistic State Space Models (SSMs) are essential for Reinforcement Learning (RL)
from high-dimensional, partial information as they provide concise representations for …
from high-dimensional, partial information as they provide concise representations for …
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
Optimal decision-making under partial observability requires reasoning about the
uncertainty of the environment's hidden state. However, most reinforcement learning …
uncertainty of the environment's hidden state. However, most reinforcement learning …
Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective
Machines that can replicate human intelligence with type 2 reasoning capabilities should be
able to reason at multiple levels of spatio-temporal abstractions and scales using internal …
able to reason at multiple levels of spatio-temporal abstractions and scales using internal …
Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion
S Guist, J Schneider, H Ma, L Chen, V Berenz… - arxiv preprint arxiv …, 2023 - arxiv.org
Operating robots precisely and at high speeds has been a long-standing goal of robotics
research. Balancing these competing demands is key to enabling the seamless …
research. Balancing these competing demands is key to enabling the seamless …
Deep Learning Methods for Intelligent Cyber-Physical Systems
SAL Chavira - 2023 - search.proquest.com
Cyber-physical systems (CPSs) have emerged in recent years as a new paradigm that
merges several technologies to allow the interface between the physical and the cybernetic …
merges several technologies to allow the interface between the physical and the cybernetic …
[HTML][HTML] Uncertainty Representations in Reinforcement Learning
CE Luis Goncalves - tuprints.ulb.tu-darmstadt.de
Reinforcement learning (RL) has achieved tremendous success over the last decade,
primarily through massive compute in simulated environments. However, applications of RL …
primarily through massive compute in simulated environments. However, applications of RL …
[معلومات الإصدار][C] Deep learning methods for intelligent cyber-physical systems
SA Langarica Chavira - 2023