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Found 7 papers, 2 papers with code
End-to-end deep metamodeling to calibrate and optimize energy loads
Finally, the optimal settings to minimize the energy loads while maintaining a target thermal comfort and air quality are obtained using a multi-objective optimization procedure.
Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications
In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas.
Model-based Reinforcement Learning
reinforcement-learning
+1
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure.
Diffusion bridges vector quantized Variational AutoEncoders
In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously.
Modular Adaptive Safety-Critical Control
We demonstrate that the unification of ISS and ISSf in an adaptive control setting allows for maintaining a single set of parameter estimates for both the CLF and CBF that can be generated by a class of update laws satisfying a few general properties.
Variational latent discrete representation for time series modelling
Discrete latent space models have recently achieved performance on par with their continuous counterparts in deep variational inference.
Last layer state space model for representation learning and uncertainty quantification
As sequential neural architectures become deeper and more complex, uncertainty estimation is more and more challenging.
