Agent Loop

Models trained to decide when to call tools.

Separates planning from execution in agent architectures.

Extending agents with long-term memory stores.

Multiple agents interacting cooperatively or competitively.

Combines value estimation (critic) with policy learning (actor).

Learning from data generated by a different policy.

Learning only from current policy’s data.

Balancing learning new behaviors vs exploiting known rewards.