Every other model here is "Supervised Learning" (learning from the past). RL is "Active Learning"—it takes actions (moves budget) to maximize a reward (Revenue), learning from trial and error.
"What is the optimal bid right now?"
"Should we explore a new channel or exploit a known winner?"
"How do we automate pacing?"
It balances Exploration (gathering info) vs Exploitation (cashing in). It views budget allocation as a sequential game against the market.
The Problem
Manual daily adjustments are slow.
What It Reveals
The optimal policy for efficient scaling.
Decision Enabled
Let the agent handle intra-day shifts.
The future of our Budget Optimizer. While currently utilizing convex optimization, we are moving toward RL for real-time, adaptive bidding agents.
A Learning Curve showing the agent's performance improving over time as it learns the market dynamics.