On-Policy Learning

Balancing learning new behaviors vs exploiting known rewards.

Continuous cycle of observation, reasoning, action, and feedback.

Models trained to decide when to call tools.

Separates planning from execution in agent architectures.

Fundamental recursive relationship defining optimal value functions.

Optimizing policies directly via gradient ascent on expected reward.

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

Learning from data generated by a different policy.