Abstract: A computer implemented method includes receiving data indicative of one or more experience tuples each comprising a first observation including a first location of an unmanned aerial vehicle, UAV, a first flight action performed by the UAV in dependence on the first observation, a reward associated with the performance of the first flight action, and a second observation including a second location of the UAV following the performance of the first action.

Abstract: There is disclosed a machine learning technique of determining a policy for an agent controlling an entity in a two-entity system. The method comprises assigning a prior policy and a respective rationality to each entity of the two-entity system, each assigned rationality being associated with a permitted divergence of a policy associated with the associated entity from the prior policy p assigned to that entity, and determining the policy to be followed by an agent corresponding to one entity by optimising an objective function F*(s), wherein the objective function F*(s) includes factors dependent on the respective rationalities and prior policies assigned to the two entities. In this way, the policy followed by an agent controlling an entity in a system can be determined taking into account the rationality of another entity within the system.

Abstract: There is described a machine learning system comprising a first subsystem and a second subsystem remote from the first subsystem. The first subsystem comprises an environment having multiple possible states and a decision making subsystem comprising one or more agents. Each agent is arranged to receive state information indicative of a current state of the environment and to generate an action signal dependent on the received state information and a policy associated with that agent, the action signal being operable to cause a change in a state of the environment. Each agent is further arranged to generate experience data dependent on the received state information and information conveyed by the action signal. The first subsystem includes a first network interface configured to send said experience data to the second subsystem and to receive policy data from the second subsystem.

Abstract: A method includes: receiving training data comprising a plurality of training data items, each training data item labelled under a respective class and comprising a elements arranged in conformity with a structured representation having an associated coordinate system; determining patches of the training data, each patch comprising a subset of the elements of a respective training data item and being associated with a location within the co-ordinate system of the structured representation; and initialising a set of parameters for a Gaussian process. The method further includes iteratively: processing pairs of the determined patches, using a patch response kernel to determine patch response data; determining, using the patch response data, entries of a covariance matrix; and updating the set of parameters in dependence on the determined entries of the covariance matrix.

Abstract: A method includes: receiving training data comprising a plurality of training data items, each training data item labelled under a respective class and comprising a elements arranged in conformity with a structured representation having an associated coordinate system; determining patches of the training data, each patch comprising a subset of the elements of a respective training data item and being associated with a location within the co-ordinate system of the structured representation; and initialising a set of parameters for a Gaussian process. The method further includes iteratively: processing pairs of the determined patches, using a patch response kernel to determine patch response data; determining, using the patch response data, entries of a covariance matrix; and updating the set of parameters in dependence on the determined entries of the covariance matrix.

Abstract: A reinforcement learning system comprises an environment (having multiple possible states), and agent, and a policy learner.. The agent is arranged to receive state information indicative of a current environment state and generate an action signal dependent on the state information and a policy associated with the agent, where the action signal is operable to cause an environment-state change. The agent is further arranged to generate experience data dependent on the state information and information conveyed by the action signal. The policy learner is configured to process the experience data in order to update the policy associated with the agent. The reinforcement learning system further comprises a probabilistic model arranged to generate, dependent on the current state of the environment, probabilistic data relating to future states of the environment, and the agent is further arranged to generate the action signal in dependence on the probabilistic data.