Abstract: Generally discussed herein are devices, systems, and methods for determining a minimal controllable latent state and operating a model trained to implement the minimal controllable latent state. A method can include receiving first and second observations of a temporal series of observations produced by a sensor, the second observation separated from the first observation by more than one other observation, the first observation associated with a first action performed by an agent responsive to the first observation, encoding, by an encoder, the first and second observations into respective first and second hidden state representations, combining the first and second hidden state representations resulting in a combined representation, operating, by an action predictor, on the combined representation resulting in a second action, and altering weights of the encoder and the action predictor based on a difference between the first and second actions.

Abstract: Systems and methods for tuning hyperparameters for a machine learning model using a challenger champion model are described. A set of challenger configurations are generated based on a hyperparameter for tuning and a subset of the set of challenger configurations are scheduled for evaluation based on a loss function. A loss value derived from the loss function for the challenger configurations is compared to a loss value derived from the loss function for a champion configuration, and the champion configuration is replaced with the challenger configuration based on the comparison of the loss value derived from the loss function for the challenger configuration and the loss value derived from the loss function for the champion configuration. When the champion is replaced, a new set of challenger configurations is generated based on the new champion configuration.