Robert Boschgmbh

Michael Volpp

Christian Daniel

Andreas Doerr

A computer-implemented method for ascertaining, using a machine learning system, a first output signal characterizing a classification and/or a regression of a first input signal, and the output signal includes a first representation which characterizes an expected value of the classification or the regression, and a second representation, which characterizes a variance of the classification or regression. The method includes: ascertaining, using an encoder, latent representations each ascertained based on a second input signal and a second output signal that corresponds to the second input signal, and the second input signal and the second output signal characterize a context, the latent representation includes a first representation characterizing an expected value and a second representation characterizing a variance; ascertaining a third representation characterizing an accumulation of the first representations; ascertaining a fourth representation characterizing an accumulation of the second representations; ascertaining the first output signal using a decoder.

Method for ascertaining an output signal with the aid of a machine learning system

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.

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Stuttgart, Germany

Michael Volpp