Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modeling agents in multi-agent systems as reinforcement learning (RL) agents and training control policies that cause the agents to cooperate towards a common goal. A method can include generating, for each of a group of simulated local agents in an agent network in which the simulated local agents share resources, information, or both, experience tuples having a state for the simulated local agent, an action taken by the simulated local agent, and a local result for the action taken, updating each local policy of each simulated local agent according to the respective local result, providing, to each of the simulated local agents, information representing a global state of the agent network, and updating each local policy of each simulated local agent according to the global state of the agent network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning shipping logistics routes. A computer-implemented method includes: receiving a request for a first shipment to occur during a first time duration, the request being provided by a requestor; obtaining shipment data representing scheduled shipments to occur during a second time duration, the second time duration overlapping the first time duration; providing the request for the first shipment and the shipment data as input to a shipping model; obtaining, as output from the shipping model, simulation results including predicted shipments during the second time duration, the predicted shipments including the first shipment and the scheduled shipments, the simulation results including predicted movements of shipping resources executing the predicted shipments during the second time duration; and assigning shipping resources to the predicted shipments based on the simulation results.

Abstract: Implementations are directed to improving network anti-fragility. In some aspects, a method includes receiving parameter data from a network of nodes, the parameter data comprising attributes, policies, and action spaces for each node in the network of nodes; configuring one or more interruptive events on one or more nodes included in the network of nodes; determining a first action of each node in the network of nodes in response to the one or more interruptive events; determining a first performance metric, for each node, that corresponds to the first action, wherein the first performance matric is determined based on at least a first reward value associated with the first action; continuously updating the first action in an iterative process to obtain a final action, wherein a performance metric corresponding to the final action satisfies a performance threshold, and transmitting the final action for each node to the network of nodes.

Abstract: Methods and systems including receiving a plurality of shipping bids from a plurality of shipping entities, each entity having goods to ship from locations to destinations, wherein each bid represents an option to ship goods at a shipping price, and wherein each bid comprises a plurality of shipping parameters; receiving a plurality of carrier bids from a plurality of carrier entities, each entity transporting the goods, wherein each bid represents an option to transport the goods at a price, and wherein each bid comprises a plurality of carrier parameters; performing a matching process to generate a plurality of pair-wise partial matches, wherein each match associates a shipping and carrier bid at a modified price, wherein the modified price is based on a deviation between the parameters; providing information representing the matches to the shipping and carrier entities; and generating training data representing which matches were exercised.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic generation of a supply chain simulation. The methods, systems, and apparatus include actions of obtaining supply chain data of a supply chain, generating a supply chain network graph that represents relationships between locations indicated by the supply chain data, determining classifications of the locations indicated by the supply chain data, determining agent rule models based on the supply chain data, and generating a supply chain simulation based on the supply chain network graph, the classifications of the locations, and the agent rule models.

Abstract: An embodiment of a synchronizer for a mobile device comprises memory for containing identification data and message data. The synchronizer is operative at least part of the time when the mobile device is inactive to establish communication with a base station, to provide from memory identification data enabling the base station to establish communication between the synchronizer and a server and, where message data is waiting to be transferred between the server and the mobile device, to enable transfer of message data waiting to be transferred. The synchronizer is operative when the mobile device is active to transfer message data between the mobile device and the memory of the synchronizer.