Glossary term
Glossary term
Agentic Systems
In agentic AI, the predefined criteria that tell the agent to stop iterating. For example, here are a few possible termination conditions:
The agent successfully completed the goal.
The agent can't use any more resources.
A human-in-the-loop has detected a problem.
In reinforcement learning, the conditions that determine when an episode ends, such as when the agent reaches a certain state or exceeds a threshold number of state transitions. For example, in tic-tac-toe (also known as noughts and crosses), an episode terminates either when a player marks three consecutive spaces or when all spaces are marked.
Created for this library
An RL team encodes episode termination conditions like goal reached or maximum steps when simulating routing problems.
A trading research team encodes termination conditions on its RL agent so each episode ends at market close.
A robotics RL team encodes termination conditions like object dropped or grasped to delimit training episodes.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License