Abstract: There is provided a computer-implemented method for updating a search distribution of an evolutionary strategies optimizer using an optimizer neural network comprising one or more attention blocks. The method comprises receiving a plurality of candidate solutions, one or more parameters defining the search distribution that the plurality of candidate solutions are sampled from, and fitness score data indicating a fitness of each respective candidate solution of the plurality of candidate solutions. The method further comprises processing, by the one or more attention neural network blocks, the fitness score data using an attention mechanism to generate respective recombination weights corresponding to each respective candidate solution. The method further comprises updating the one or more parameters defining the search distribution based upon the recombination weights applied to the plurality of candidate solutions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using an action selection neural network that performs in-context reinforcement learning when controlling an agent on a new task.

Abstract: In one aspect there is provided a method for training a neural network system by reinforcement learning. The neural network system may be configured to receive an input observation characterizing a state of an environment interacted with by an agent and to select and output an action in accordance with a policy aiming to satisfy an objective. The method may comprise obtaining a policy set comprising one or more policies for satisfying the objective and determining a new policy based on the one or more policies. The determining may include one or more optimization steps that aim to maximize a diversity of the new policy relative to the policy set under the condition that the new policy satisfies a minimum performance criterion based on an expected return that would be obtained by following the new policy.

Abstract: A reinforcement learning system, method, and computer program code for controlling an agent to perform a plurality of tasks while interacting with an environment. The system learns options, where an option comprises a sequence of primitive actions performed by the agent under control of an option policy neural network. In implementations the system discovers options which are useful for multiple different tasks by meta-learning rewards for training the option policy neural network whilst the agent is interacting with the environment.

Abstract: There is described methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The reinforcement learning system comprises an agent configured to perform actions based upon a policy and an intrinsic reward system configured to generate intrinsic reward values for the agent based upon the actions taken by the agent. The method comprises training the reinforcement learning system based upon a plurality of tasks. The training comprises updating the agent's policy based upon the intrinsic reward values generated by the intrinsic reward system and updating the intrinsic reward system based upon an extrinsic reward value obtained based upon the task being performed by the agent. The training further comprises re-initializing the agent's policy when an expiration criterion associated with the agent is met.

Abstract: A system that monitors and assesses the moods of subjects with neurological disorders, like bipolar disorder, by analyzing normal conversational speech to identify speech data that is then analyzed through an automated speech data classifier. The classifier may be based on a vector, separator, hyperplane, decision boundary, or other set of rules to classify one or more mood states of a subject. The system classifier is used to assess current mood state, predicted instability, and/or a change in future mood state, in particular for subjects with bipolar disorder.

Abstract: A system that monitors and assesses the moods of subjects with neurological disorders, like bipolar disorder, by analyzing normal conversational speech to identify speech data that is then analyzed through an automated speech data classifier. The classifier may be based on a vector, separator, hyperplane, decision boundary, or other set of rules to classify one or more mood states of a subject. The system classifier is used to assess current mood state, predicted instability, and/or a change in future mood state, in particular for subjects with bipolar disorder.