Pre-Publication V2. A machine learning method where an AI system is trained learns to make decisions through trial and error, improving its performance by interacting with a specific system or other context environment. This learning approach enables the AI agent to run make a series of operations decisions to maximize the cumulative reward signal for the task without human intervention and without explicitly programming of each action needed for the specified objective the agent to achieve a goal.
Deliberation Summary:
Consistency in reference to AI “systems”.
Pre-Publication V1. A machine learning method where an AI learns to make decisions through trial and error, improving its performance by interacting with a specific environment. This learning approach enables the agent to make a series of decisions to maximize the cumulative reward for the task without human intervention and without explicitly programming the agent to achieve a goal.