Abstract: Aspects of the present disclosure provide systems and methods which utilizes machine learning techniques to provide enhanced accessibility features to a game. An accessibility service is provided which is capable of instantiating one or more machine learning models which can process current gameplay states and generate commands to assist users during gameplay. The accessibility commands may be provided to a game and used to supplement or modify user provided inputs in order to compensate for specific user needs. In further aspects, an accessibility user interface is provided which allows a user to dynamically enable or disable accessibility features during gameplay. The user interface is operable to receive accessibility selections and provide the selection data to an accessibility service during gameplay.

Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.

Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.

Abstract: In various examples there is a method performed by a computer-implemented agent in an environment. The method comprises storing a reinforcement learning policy for controlling the computer-implemented agent. The method also comprises storing a distribution as a latent representation of a belief of the computer-implemented agent about at least one other agent in the environment. The method involves executing the computer-implemented agent according to the policy conditioned on parameters characterizing the distribution.

Abstract: An apparatus is described for training a behavior of an agent in a physical or digital environment. The apparatus comprises a memory storing the location of at least one reward token in the environment. The location has been specified by a user. At least one processor executes the agent in the environment according to a behavior policy. The processor is configured to observe values of variables comprising: an observation of the agent, an action of the agent and any reward resulting from the reward token. The processor is configured to update the behavior policy using reinforcement learning according to the observed values.

Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.

Abstract: An apparatus is described for training a behavior of an agent in a physical or digital environment. The apparatus comprises a memory storing the location of at least one reward token in the environment. The location has been specified by a user. At least one processor executes the agent in the environment according to a behavior policy. The processor is configured to observe values of variables comprising: an observation of the agent, an action of the agent and any reward resulting from the reward token. The processor is configured to update the behavior policy using reinforcement learning according to the observed values.

Abstract: Systems for managing content in a cloud-based service platform. A server in a cloud-based environment is interfaced with storage devices that hold one or more stored objects accessible by two or more users. The stored objects comprise folders and files as well as other objects such as workflow objects that are associated with the folders or the files. The workflow objects comprise workflow metadata that describes a workflow as a set of workflow tasks to be carried out in a progression. Processing of a workflow task and/or carrying out a portion of the progression includes modification of shared content objects. The processing or modification events are detected through workflow events, which in turn cause one or more workflow responses to be generated. Workflow responses comprise updates to the workflow metadata to record progression through the workflow and/or workflow responses comprise updates to any one or more of the stored objects.

Abstract: Systems for managing content in a cloud-based service platform. A server in a cloud-based environment is interfaced with storage devices that hold one or more stored objects accessible by two or more users. The stored objects comprise folders and files as well as other objects such as workflow objects that are associated with the folders or the files. The workflow objects comprise workflow metadata that describes a workflow as a set of workflow tasks to be carried out in a progression. Processing of a workflow task and/or carrying out a portion of the progression includes modification of shared content objects. The processing or modification events are detected through workflow events, which in turn cause one or more workflow responses to be generated. Workflow responses comprise updates to the workflow metadata to record progression through the workflow and/or workflow responses comprise updates to any one or more of the stored objects.

Abstract: Systems for managing content in a cloud-based service platform. A server in a cloud-based environment is interfaced with storage devices that hold one or more stored objects accessible by two or more users. The stored objects comprise folders and files as well as other objects such as workflow objects that are associated with the folders or the files. The workflow objects comprise workflow metadata that describes a workflow as a set of workflow tasks to be carried out in a progression. Processing of a workflow task and/or carrying out a portion of the progression includes modification of shared content objects. The processing or modification events are detected through workflow events, which in turn cause one or more workflow responses to be generated. Workflow responses comprise updates to the workflow metadata to record progression through the workflow and/or workflow responses comprise updates to any one or more of the stored objects.

Abstract: Systems for managing content in a cloud-based service platform. A server in a cloud-based environment is interfaced with storage devices that hold one or more stored objects accessible by two or more users. The stored objects comprise folders and files as well as other objects such as workflow objects that are associated with the folders or the files. The workflow objects comprise workflow metadata that describes a workflow as a set of workflow tasks to be carried out in a progression. Processing of a workflow task and/or carrying out a portion of the progression includes modification of shared content objects. The processing or modification events are detected through workflow events, which in turn cause one or more workflow responses to be generated. Workflow responses comprise updates to the workflow metadata to record progression through the workflow and/or workflow responses comprise updates to any one or more of the stored objects.