Reinforcement learning simulation of supply chain graph
US12399957B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 6, 2021 |
| Grant date | Aug 26, 2025 |
| Priority date | — |
| Expiry date | May 29, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/295
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.