HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
Quick Answer
This paper shows that HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs.
Quick Take
It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.
Key Points
- HOBA decouples strategic reasoning, model selection, and bid execution across three time scales.
- A large language model infers hyperparameters through a Think-Act-Observe-Reflect loop.
- The SARSA agent incorporates causal adjustment to eliminate selection bias among expert models.
- Dynamic expert pool includes PID, MPC, IQL, and Decision Transformer for bid execution.
- HOBA achieved a +3.6% increase in target cost during large-scale online deployment.
DeepSignal Analysis
What happened
The paper introduces HOBA, a hierarchical reinforcement learning framework designed to enhance online advertising bidding systems. It addresses issues of adaptability in non-stationary auction markets and reduces the need for manual hyperparameter tuning. HOBA employs a large language model for hyperparameter inference and a SARSA agent for expert model selection.
Key evidence
- HOBA utilizes a large language model to infer hyperparameters from contextual signals, improving adaptability in online advertising.
- The framework includes a SARSA agent that dynamically selects among expert models, which helps eliminate selection bias.
- In large-scale deployment, HOBA achieved a +3.6% increase in target cost, demonstrating its effectiveness compared to state-of-the-art baselines.
Why it matters
The introduction of HOBA could significantly impact online advertising by providing a more adaptive and efficient bidding strategy. By reducing the reliance on manual hyperparameter tuning, it lowers operational costs and enhances performance in dynamic auction environments. The reported improvements over existing methods suggest that HOBA may set a new standard in the field.
Paper Resources
Source Excerpt
Online advertising bidding systems typically deploy multiple offline-trained expert models (e. g. , PID controllers, model predictive control, offline RL policies) but face two critical limitations: lack of online adaptability to non-stationary auction markets, and reliance on costly manual tuning of hyperparameters such as bid bounds and budget pacing constraints. We propose HOBA (Hierarchical On-policy Bidding Agents), a hierarchical reinforcement learning framework that decouples strategic reas
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