Abstract: Processing devices and methods for initiating interactions with non-player characters in a virtual environment. An example processing device includes a reception unit configured to receive one or more avatar properties associated with an avatar of a user; the reception unit is configured to receive one or more non-player character, NPC, properties associated with a given NPC; a calculation unit configured to calculate, in dependence upon the received avatar properties and the received NPC properties, a probability of the user desiring to initiate an interaction between the avatar and the given NPC; and an initiation unit configured to, in response to the calculated probability being above a predetermined threshold, automatically initiate the interaction between the avatar and the given NPC.

Abstract: A data processing apparatus including circuitry that obtains virtual object data representing a virtual object of a video game; inputs the virtual object data to a machine learning (ML) model that determines one or more interaction regions of the virtual object, the one or more interaction regions being regions of the virtual object which can be interacted with by a character of the video game; and obtains, as an output of the ML model, interaction region data representing the determined one or more interaction regions.

Abstract: A data processing apparatus comprising circuitry configured to: execute a machine learning, ML, model configured to receive, as an input, a game state of a video game and first virtual physiological data indicative of a first virtual physiological state of an agent of the video game, and generate, as an output, a probability of each of a plurality of actions of the agent and second virtual physiological data indicative of a second, subsequent, virtual physiological state of the agent; and perform reinforcement learning to generate a policy for completion of a task by the agent, the reinforcement learning comprising, for each of a plurality of attempts at the task by the agent, executing one or more successive iterations of the ML model and, for each attempt, controlling the agent to perform a different respective set of actions based on the output probability of each of the plurality of actions at each of the one or more successive iterations.

Abstract: A system for training a machine learning model comprises a receiving unit configured to receive first data comprising an attribute sequence providing a sequence of values each indicating whether a particular attribute is associated with a respective one of a sequence of training frames, training frames so associated being referred to as key frames, a training unit configured to train the machine learning model, using a training dataset, to generate behaviour for an agent to predict occurrence of the particular attribute within a sequence of action frames, and a filtering unit configured to apply a filter to the attribute sequence to generate the training dataset, wherein the filter modifies values of the attribute sequence for at least some training frames proximate to a given key frame, to provide context information in the training dataset indicating proximity to a key frame for frames within the sequence of training frames.

Abstract: There is provided an audio processing method for assisting communication between a plurality of users of a videogame. The method comprises: receiving, from one or more sensors, data relating to one or more lip movements of a first user of the plurality of users; detecting a game state of the videogame; determining, using a machine learning model, an intended speech input by the first user in dependence on: the data relating to lip movements of the first user, and the game state; generating an audio signal corresponding to the intended speech input by the first user; and outputting the audio signal to a device of a second user of the plurality of users.

Abstract: A data processing apparatus comprising circuitry configured to: obtain gameplay data indicating in-game behaviour of a human video game player; configure, using the obtained gameplay data, an artificial intelligence, AI, agent representing the human video game player; execute one or more AI agent video game sessions including the AI agent and obtain AI agent performance data indicating in-game performance of the AI agent during the one or more AI agent video game sessions; and perform matchmaking of the human video game player with another human video game player based on the obtained AI agent performance data.

Abstract: A method of generating a training system comprising, for a set of states and corresponding actions, training separate auto encoders; using interim encoded representations from each trained auto encoder as input to a machine learning recoder, wherein each recoder is trained with a respective multi-part loss function that discriminates output representations for different respective inputs within each recoder whilst converging representations for parallel current state and action inputs between the recoders.

Abstract: A method of training an autonomous agent is provided, the method comprising: providing videogame data generated by a human playing a videogame as input data to a training network for training an autonomous agent for playing the videogame; generating videogame data of the trained autonomous agent playing the videogame; providing the videogame data of the trained autonomous agent playing the videogame data to a discriminator of a generative adversarial network ‘GAN’, the discriminator of the GAN being trained to distinguish videogame data of a human playing the videogame and videogame data of an autonomous agent playing the videogame; generating a classification, by the discriminator of the GAN, of the output videogame data of the trained autonomous agent playing the videogame as human or agent; and updating at least one of the training network and the discriminator of the GAN based on the classification generated by the discriminator of the GAN.

Abstract: A data processing apparatus comprises a captioning model to receive gameplay telemetry data indicative of one or more in-game properties for a session of a video game, the captioning model comprising an artificial neural network (ANN) trained to output caption data comprising one or more captions in dependence upon a learned mapping between gameplay telemetry data and caption data, one or more of the captions comprising one or more words for providing a visual description for the session of the video game, and output circuitry to output one or more of the captions.

Abstract: A decoder apparatus comprises receiving circuitry to receive caption data indicative of a language-based description for a first image and encoded data representative of the first image; and decoder circuitry comprising one or more trained machine learning models operable to generate a reconstructed image in dependence on the caption data and the encoded data, the reconstructed image having a higher image quality than an image quality associated with the encoded data representative of the first image.