Abstract: A computer implemented method for configuring a reinforcement learning agent to perform an efficient reinforcement learning procedure, wherein the reinforcement learning agent comprises a model trained using a machine learning process to determine actions to be performed by the reinforcement learning agent. The method comprises using the model to determine an action to perform, based on values of a set of features obtained in an environment; determining, for a first feature in the set of features, a first indication of a relative contribution of the first feature, compared to other features in the set of features, to the determination of the action by the model; and determining a reward to be given to the reinforcement learning agent in response to performing the action, based on the first feature and the first indication.

Abstract: The present invention is concerned with a catheter which includes an inflatable balloon operable to retain the catheter in position during use, and which is located and configured to both reduce discomfort during use by preventing contact between the end of the catheter and the urinary tract/bladder wall, and reduce the force required to remove the catheter by reducing cuffing of the collapsed balloon.

Abstract: The present invention is concerned with a catheter which includes an inflatable balloon operable to retain the catheter in position during use, and which is located and configured to both reduce discomfort during use by preventing contact between the end of the catheter and the urinary tract/bladder wall, and reduce the force required to remove the catheter by reducing cuffing of the collapsed balloon.