Abstract: A computer-implemented method for for learning a policy. The method includes: recording at least an episode of interactions of the agent with its environment following policy and adding the recorded episode to a set of training data; optimizing a transition dynamics model based on the training data such that the transition dynamics model predicts the next states of the environment depending on the states and actions contained in the training data; optimizing policy parameters based on the training data and the transition dynamics model by optimizing a reward. In the method, the transition dynamics model comprises a first model characterizing the global model and a second model characterizing a correction model, which is configured to correct outputs of the first model.

Abstract: A method for configuring a technical system. The method includes: detecting reference observations; conditioning reference system models on the reference observations detected for the reference system; detecting observations of results of the technical system to be configured for different values of the configuration parameters; adjusting an a priori model for the relationship between the values of the configuration parameters and the results provided by the technical system to the observations detected for the technical system, wherein the a priori model is formed from a weighted combination of the conditioned reference system models; ascertaining an a posteriori model for the relationship between the values of the configuration parameters and the results provided by the technical system by conditioning the adjusted a priori model on the observations detected for the technical system to be configured; and configuring the technical system using the ascertained a posteriori model.

Abstract: A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.

Abstract: A method for training a machine learning model. The method includes: ascertaining, for each of a plurality of training data elements, a gradient of a target function, wherein the gradient comprises a component for each of a plurality of parameters of the machine learning model; generating an overall gradient by averaging the ascertained gradients component-wise by summing, for each component, the values of the ascertained gradients for this component and by dividing a resulting sum for the component by the number of ascertained gradients for which the component is above a specified threshold value; and adjusting the machine learning model in a direction given by the overall gradient.

Abstract: A computer-implemented method of learning a policy for an agent. The method includes: receiving an initialized first neural network, in particular a Q-functionor value-function, an initialized second neural network, auxiliary parameters, and the initialized policy; repeating the following steps until a termination condition is fulfilled: sampling a plurality of pairs of states, actions, rewards and new states from a storage. Sampling actions for the current states, and actions for the new sampled states; computing features from a penultimate layer of the first neural network based on the sampled states and actions and updating the second neural network and the auxiliary parameters as well as updating parameters the first neural network using a re-weighted loss.

Abstract: A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.

Abstract: A method for training a control policy. The method includes estimating the variance of a value function which associates a state with a value of the state or a pair of state and action with a value of the pair by solving a Bellman uncertainty equation, wherein, for each of multiple states, the reward function of the Bellman uncertainty equation is set to the difference of the total uncertainty about the mean of the value of the subsequent state following the state and the average aleatoric uncertainty of the value of the subsequent state and biasing the control policy in training towards regions for which the estimation gives a higher variance of the value function than for other regions.

Abstract: A computer-implemented control method of constrained controlling of a computer-controlled system. The system is controlled according to a control input, which is safe if a constraint quantity resulting from the controlling of the computer-controlled system exceeds a constraint threshold. A current control input is determined based on previous control inputs and corresponding previous noisy measurements. The computer-controlled system is controlled according to the current control input, thereby obtaining a current noisy measurement of the resulting constraint quantity. The current control input is determined based on a mutual information between a first random variable representing the constraint quantity resulting from the current control input and a second random variable indicating whether a further control input is safe.

Abstract: A system, a device and a method for controlling a physical or chemical process. The method includes: determining a second a posteriori model based on a first a posteriori model that describes the relationship between an input variable and an output variable of a process related to the physical/chemical process, the second a posteriori model describing the relationship between an input variable and an output variable of the physical or chemical process; and controlling the physical or chemical process using the second a posteriori model.

Abstract: A method for estimating input certainty for a neural network using generative modeling. The method includes generating, using an input data, two or more input data and embedding vector combinations and providing, at the neural network, each of the two or more input data and embedding vector combinations. The method also includes receiving, from the neural network, an output value for each input data and embedding vector combination of the two or more input data and embedding vector combinations. The method also includes computing a variance value for the output values of each respective input data and embedding vector combinations and determining a certainty value for the input data based on the variance value.

Abstract: A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.

Abstract: A computer-implemented method for for learning a policy. The method includes: recording at least an episode of interactions of the agent with its environment following policy and adding the recorded episode to a set of training data; optimizing a transition dynamics model based on the training data such that the transition dynamics model predicts the next states of the environment depending on the states and actions contained in the training data; optimizing policy parameters based on the training data and the transition dynamics model by optimizing a reward. In the method, the transition dynamics model comprises a first model characterizing the global model and a second model characterizing a correction model, which is configured to correct outputs of the first model.

Abstract: A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.