Abstract: A method and system for a training manager for generating diagnosis models for mobile networks. The method including selecting automatically a set of parameters for an action to be simulated in a simulated network where the simulated network replicates a target network for a diagnosis model, executing a simulation of an operation of a network based on the set of parameters of the action to generate an output of the simulation including a set of network performance metrics, transforming output of the simulation into training data for the diagnosis model, training the diagnosis model with the training data, and outputting the diagnosis model for the target network, in response to the diagnosis model meeting a designated quality threshold.

Abstract: A method of a node in a radio access network that optimizes radio access network operations. The method of the node includes determining a topology of the radio access network, exchanging optimization information and network metric information with neighbor nodes in the topology, determining an updated local configuration for the node based on a negotiated optimization with the neighbor nodes, and updating an optimization function based on collected updated network metric information of the node executing the updated local configuration.

Abstract: Systems and methods for providing navigation to a vehicle may include receiving observation data from one or more sensors of the vehicle, generating projection data corresponding to the one or more traffic participants based on the observation data for each time step within a time period, and predicting interactions between the vehicle, the one or more traffic participants, and the one or more obstacles, based on the projection data of the one or more traffic participants. The systems and methods may further include determining a set of actions by the vehicle corresponding to a probability of the vehicle safely arriving at a target location based on the predicted interactions, and selecting one or more actions from the set of actions and provide the one or more actions to a navigation system of the vehicle, wherein the navigation system uses the navigation data to provide navigation instructions to the vehicle.

Abstract: According to one aspect, a system for reinforcement learning with iterative reasoning may include a memory for storing computer readable code and a processor operatively coupled to the memory, the processor configured to receive a level-0 policy and a desired reasoning level n. The processor may repeat for k=1 . . . n times, the following: populate a training environment with a level-(k?1) first agent, populate the training environment with a level-(k?1) second agent, and train a level-k agent based on the level-(k?1) first agent and the level-(k?1) second agent to derive a level-k policy.

Abstract: Systems and methods for providing navigation to a vehicle may include receiving observation data from one or more sensors of the vehicle, generating projection data corresponding to the one or more traffic participants based on the observation data for each time step within a time period, and predicting interactions between the vehicle, the one or more traffic participants, and the one or more obstacles, based on the projection data of the one or more traffic participants. The systems and methods may further include determining a set of actions by the vehicle corresponding to a probability of the vehicle safely arriving at a target location based on the predicted interactions, and selecting one or more actions from the set of actions and provide the one or more actions to a navigation system of the vehicle, wherein the navigation system uses the navigation data to provide navigation instructions to the vehicle.