Abstract: A method, for a mobile computing device, of installing a sensor device, the method including the steps of obtaining, from the sensor device, information indicative of an identification of the sensor device; obtaining information indicative of a location of a piece of hygiene equipment with which the sensor device is to be associated; and associating the information indicative of an identification of the sensor device with the information indicative of a location of the piece of hygiene equipment.

Abstract: A computer-implemented method comprises instantiating a policy function approximator. The policy function approximator is configured to calculate a plurality of estimated action probabilities in dependence on a given state of the environment. Each of the plurality of estimated action probabilities corresponds to a respective one of a plurality of discrete actions performable by the reinforcement learning agent within the environment. An initial plurality of estimated action probabilities in dependence on a first state of the environment are calculated. Two or more of the plurality of discrete actions are concurrently performed within the environment when the environment is in the first state. In response to the concurrent performance, a reward value is received. In response to the received reward value being greater than a baseline reward value, the policy function approximator is updated, such that it is configured to calculate an updated plurality of estimated action probabilities.

Abstract: A method, for a mobile computing device, of installing a sensor device, the method including the steps of obtaining, from the sensor device, information indicative of an identification of the sensor device; obtaining information indicative of a location of a piece of hygiene equipment with which the sensor device is to be associated; and associating the information indicative of an identification of the sensor device with the information indicative of a location of the piece of hygiene equipment.

Abstract: A computer-implemented method comprises instantiating a policy function approximator. The policy function approximator is configured to calculate a plurality of estimated action probabilities in dependence on a given state of the environment. Each of the plurality of estimated action probabilities corresponds to a respective one of a plurality of discrete actions performable by the reinforcement learning agent within the environment. An initial plurality of estimated action probabilities in dependence on a first state of the environment are calculated. Two or more of the plurality of discrete actions are concurrently performed within the environment when the environment is in the first state. In response to the concurrent performance, a reward value is received. In response to the received reward value being greater than a baseline reward value, the policy function approximator is updated, such that it is configured to calculate an updated plurality of estimated action probabilities.

Abstract: An information display device including a display displays content for conveying content to a user configured to adapt the content conveyed to the user by changing the content displayed on the display based on a received or determined detection result.