Robert Boschgmbh

Max-planck-gesellschaft Zur Forderung Der Wissenschaften E.v.

Christian Daniel

Martin Schiegg

Andreas Doerr

Edgar Klenske

Jeremy Zico Kolter

Heiner Markert

Dotan Di Castro

Devin T Willmott

Volker Imhof

Stefan Angermaier

Zohar Feldman

The Duy Nguyen-Tuong

Lukas Froehlich

Maksym Lefarov

Joel Oren

Sebastian Trimpe

Michael Volpp

Ayal Taitler

Nima Manafzadeh Dizbin

A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

Device and method for scheduling a set of jobs for a plurality of machines

Methods for ascertaining a control strategy for a technical system using a Bayesian optimization method. The control strategy is created based on model parameters of a control model and is executable. The method includes providing a quality function whose shape corresponds to a regression function and that evaluates a quality of a controlling of the technical system based on model parameters; carrying out a Bayesian optimization method based on the quality function in order to iteratively ascertain a model parameter set having model parameters within a model parameter domain that indicates the permissible value ranges for the model parameters; and determining the model parameter domain for at least one of the model parameters as a function of an associated maximum a posteriori estimated value of the quality function.

Method and device for determining model parameters for a control strategy for a technical system with the aid of a Bayesian optimization method

A computational method for training a meta-learned, evolution strategy black box optimization classifier. The method includes receiving one or more training functions and one or more initial meta-learning parameters of the optimization classifier. The method further includes sampling a sampled objective function from the one or more training functions and an initial mean of the sampled function. The method also includes computing a set of T number of means by running the meta learned, evolution strategy classifier on the sampled objective function using the initial mean for T number of steps in t=1, . . . , T. The method also includes computing a loss function from the set of T number of means. The method further includes updating the one or more initial meta-learning parameters of the optimization classifier in response to a characteristic of the loss function.

Meta-learned, evolution strategy black box optimization classifiers

A system is described for configuring another system, e.g., a robotics system. The other system interacts with an environment according to a deterministic policy by repeatedly obtaining, from a sensor, sensor data indicative of a state of the environment, determining a current action, and providing, to an actuator, actuator data causing the actuator to effect the current action in the environment. To configure the other system, the system optimizes a loss function based on an accumulated reward distribution with respect to a set of parameters of the policy. The accumulated reward distribution includes an action probability of an action of a previous interaction log being performed according to the current set of parameters. The action probability is approximated using a probability distribution defined by an action selected by the deterministic policy according to the current set of parameters.

Configuring a system which interacts with an environment

A method is provided for ascertaining a NOconcentration and an NHslip downstream from an SCR catalytic converter of an internal combustion engine of a vehicle. State variables of an internal combustion engine as first input variables and an updated NHfill level of the SCR catalytic converter as a second input variable cooperate with at least one machine learning algorithm or at least one stochastic model. The at least one machine learning algorithm or at least one stochastic model calculates the NOconcentration and the NHslip downstream from the SCR catalytic converter as a function of the first input variables and the second input variables and output the same as output variables.

Method for ascertaining a NOconcentration and a NHslip downstream from an SCR catalytic converter

A method for ascertaining a time characteristic of a measured variable adjustable by an actuator, wherein a time characteristic of a control variable is applied to the actuator, wherein the ascertaining is effected by means of a Gaussian process state model of the behavior of the actuator, wherein the time characteristic of the measured variable of the actuator is ascertained on the basis of a parameterizable family of functions, wherein in the parameterizable family of functions a time dependency of a later latent state, in particular ascertained using a transfer function, of the actuator on an earlier latent state of the actuator and an earlier control variable of the actuator is the same as the applicable dependency of the Gaussian process state model.

Method for ascertaining a time characteristic of a measured variable, prediction system, actuator control system, method for training the actuator control system, training system, computer program, and machine-readable storage medium

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Leonberg, Germany

Christian Daniel