Abstract: A system and method are disclosed to train machine learning models, generate software agents, and evaluate, via reinforcement learning, the actions of the software agents in a simulated ecosystem. Embodiments include a computer comprising a processor and memory and configured to train one or more machine learning models to generate one or more software agents, wherein each software agent comprises an autonomous software program designed to execute a task in a supply chain network. Embodiments generate a first software agent and a second software agent, and a simulated supply chain ecosystem representing a hierarchical structure of supply chain network tasks. Embodiments simulate one or more tasks executed by the software agents in the simulated supply chain ecosystem, review the tasks according to one or more defined objectives, and apply reinforcement incentives to the software agents.

Abstract: A system and method are disclosed to train machine learning models, generate software agents, and evaluate, via reinforcement learning, the actions of the software agents in a simulated ecosystem. Embodiments include a computer comprising a processor and memory and configured to train one or more machine learning models to generate one or more software agents, wherein each software agent comprises an autonomous software program designed to execute a task in a supply chain network. Embodiments generate a first software agent and a second software agent, and a simulated supply chain ecosystem representing a hierarchical structure of supply chain network tasks. Embodiments simulate one or more tasks executed by the software agents in the simulated supply chain ecosystem, review the tasks according to one or more defined objectives, and apply reinforcement incentives to the software agents.