Microsoft Technology Licensing, LLC

Jeremy Randall Reynolds

Ranveer Chandra

Vaishnavi Nattar Ranganathan

Peeyush Kumar

Vishal Jain

Michael McNab Bassani

Hui Qing Li

Lillian Jane Ratliff

A computing system including a processor configured to receive training data including, for each of a plurality of training timesteps, training forecast states associated with respective training-phase agents included in a training supply chain graph. The processor may train a reinforcement learning simulation of the training supply chain graph using the training data via policy gradient reinforcement learning. At each training timestep, the training forecast states may be shared between the training-phase agents during training. The processor may receive runtime forecast states associated with respective runtime agents included in a runtime supply chain graph. For a runtime agent, at the trained reinforcement learning simulation, the processor may generate a respective runtime action output associated with a corresponding runtime forecast state of the runtime agent based at least in part on the runtime forecast states. The processor may output the runtime action output.

Reinforcement learning simulation of supply chain graph

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Boulder, CO, United States of America

Jeremy Randall Reynolds