Efficient gameplay training for artificial intelligence

A server, comprising:

• a network interface;
• one or more processors;
• and
• a memory storing a set of executable instructions, the set of executable instructions to manipulate the one or more processors to:
• generate, based at least in part on control information associating each of one or more output states of a gaming application with an input variable, a gameplay data model for the gaming application;
• provide, via a programmatic interface, the generated gameplay data model to an actor component executing on a remote client computing device;
• receive, from the actor component and via the programmatic interface, observational data generated from artificial gameplay actions performed within the gaming application by the actor component based on inferences generated by the actor component using the generated gameplay data model;
• modify the generated gameplay data model based on the received observational data;
• and
• provide, to the actor component and via the programmatic interface, the modified gameplay data model for use by the actor component in performing additional artificial gameplay actions within the gaming application.

A method, comprising:

• receiving, by an actor component executed by one or more processors and via a programmatic interface from a machine learning component executing on one or more remote server computing systems, a gameplay data model for a gaming application;
• executing, by the one or more processors, an instance of the gaming application;
• providing, to the machine learning component and via the programmatic interface, observational data generated from artificial gameplay actions performed within the executing instance of the gaming application by the actor component based at least in part on inferences generated by the actor component using the gameplay data model;
• and
• receiving, from the machine learning component executing on the one or more remote server computing systems and via the programmatic interface, a modified gameplay data model based at least in part on the provided observational data.