Systems and methods for lam simulator self learning framework for an agent with real-time exploration and high-quality feedback automation

A system comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor and configure to store instructions that cause the at least one processor to perform operations, the operations comprising:
• receiving at a large action model simulator framework, a content dataset associated with a task, wherein the content dataset includes a task name of the task and at least one user command parameter;
• identifying an abstract task based on the task name in the content dataset;
• identifying at least one task available tool;
• generating a user command for an artificial intelligence agent using the abstract task and the at least one user command parameter in the content dataset, wherein the user command instructs the artificial intelligence agent to complete the task;
• training the artificial intelligence agent to execute the task over a plurality of iterations, wherein an iteration in the plurality of iterations comprises:
• creating a conversation data object from the user command and the at least one task available tool;
• generating, using a large language model in the artificial intelligence agent and the conversation data object, an action plan comprising at least one action;
• executing the at least one action in the action plan using at least one environment and the at least one task available tool;
• and
• evaluating the at least one action in the action plan.

A non-transitory computer readable medium having instructions stored thereon, that when executed by a processor cause the processor to perform operations, the operations comprising:

• receiving, via a data interface, at a large action model simulator framework, a content dataset associated with a task, wherein the content dataset includes a task name of the task and at least one user command parameter;
• identifying an abstract task based on the task name in the content dataset;
• identifying at least one task available tool;
• generating a user command for an artificial intelligence agent using the abstract task and the at least one user command parameter in the content dataset, wherein the user command instructs the artificial intelligence agent to complete the task;
• training the artificial intelligence agent to execute the task over a plurality of iterations, wherein an iteration in the plurality of iterations comprises:
• creating a conversation data object from the user command and the at least one task available tool;
• generating, using a generative language model in the artificial intelligence agent and the conversation data object, an action plan comprising at least one action;
• executing the at least one action in the action plan using at least one environment and the at least one task available tool;
• and
• evaluating the at least one action in the action plan.