AdCraft: An Advanced Reinforcement Learning Benchmark Environment for
Search Engine Marketing Optimization
- URL: http://arxiv.org/abs/2306.11971v3
- Date: Tue, 14 Nov 2023 21:01:26 GMT
- Title: AdCraft: An Advanced Reinforcement Learning Benchmark Environment for
Search Engine Marketing Optimization
- Authors: Maziar Gomrokchi, Owen Levin, Jeffrey Roach, Jonah White
- Abstract summary: We introduce AdCraft, a novel benchmark environment for the Reinforcement Learning (RL) community.
The environment simulates bidding and budgeting dynamics within Search Engine Marketing (SEM), a digital marketing technique.
We demonstrate the challenges imposed on agent convergence and performance by sparsity and non-stationarity.
- Score: 0.6554326244334868
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce AdCraft, a novel benchmark environment for the Reinforcement
Learning (RL) community distinguished by its stochastic and non-stationary
properties. The environment simulates bidding and budgeting dynamics within
Search Engine Marketing (SEM), a digital marketing technique utilizing paid
advertising to enhance the visibility of websites on search engine results
pages (SERPs). The performance of SEM advertisement campaigns depends on
several factors, including keyword selection, ad design, bid management, budget
adjustments, and performance monitoring. Deep RL recently emerged as a
potential strategy to optimize campaign profitability within the complex and
dynamic landscape of SEM, but it requires substantial data, which may be costly
or infeasible to acquire in practice. Our customizable environment enables
practitioners to assess and enhance the robustness of RL algorithms pertinent
to SEM bid and budget management without such costs. Through a series of
experiments within the environment, we demonstrate the challenges imposed on
agent convergence and performance by sparsity and non-stationarity. We hope
these challenges further encourage discourse and development around effective
strategies for managing real-world uncertainties.
Related papers
- Rethinking and Benchmarking Predict-then-Optimize Paradigm for
Combinatorial Optimization Problems [62.25108152764568]
Many web applications rely on solving optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks.
We consider the performance of prediction and decision-making in a unified system.
We provide a comprehensive categorization of current approaches and integrate existing experimental scenarios.
arXiv Detail & Related papers (2023-11-13T13:19:34Z) - Online Advertisements with LLMs: Opportunities and Challenges [51.96140910798771]
This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems.
We delve into essential requirements including privacy, latency, reliability as well as the satisfaction of users and advertisers that such a system must fulfill.
arXiv Detail & Related papers (2023-11-11T02:13:32Z) - Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena [25.865825113847404]
We introduce AucArena, a novel evaluation suite that simulates auctions.
We conduct controlled experiments using state-of-the-art Large Language Models (LLMs) to power bidding agents to benchmark their planning and execution skills.
arXiv Detail & Related papers (2023-10-09T14:22:09Z) - Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with
Online Learning [60.17407932691429]
Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability.
We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments.
We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
arXiv Detail & Related papers (2023-09-04T17:30:21Z) - Adversarial Constrained Bidding via Minimax Regret Optimization with
Causality-Aware Reinforcement Learning [18.408964908248855]
Existing approaches on constrained bidding typically rely on i.i.d. train and test conditions.
We propose a practical Minimax Regret Optimization (MiRO) approach that interleaves between a teacher finding adversarial environments for tutoring and a learner meta-learning its policy over the given distribution of environments.
Our method, MiRO with Causality-aware reinforcement Learning (MiROCL), outperforms prior methods by over 30%.
arXiv Detail & Related papers (2023-06-12T13:31:58Z) - A Unified Framework for Campaign Performance Forecasting in Online
Display Advertising [9.005665883444902]
Interpretable and accurate results could enable advertisers to manage and optimize their campaign criteria.
New framework reproduces campaign performance on historical logs under various bidding types with a unified replay algorithm.
Method captures mixture calibration patterns among related forecast indicators to map the estimated results to the true ones.
arXiv Detail & Related papers (2022-02-24T03:04:29Z) - Bid Optimization using Maximum Entropy Reinforcement Learning [0.3149883354098941]
This paper focuses on optimizing a single advertiser's bidding strategy using reinforcement learning (RL) in real-time bidding (RTB)
We first utilize a widely accepted linear bidding function to compute every impression's base price and optimize it by a mutable adjustment factor derived from the RTB auction environment.
Finally, the empirical study on a public dataset demonstrates that the proposed bidding strategy has superior performance compared with the baselines.
arXiv Detail & Related papers (2021-10-11T06:53:53Z) - Scaling up Search Engine Audits: Practical Insights for Algorithm
Auditing [68.8204255655161]
We set up experiments for eight search engines with hundreds of virtual agents placed in different regions.
We demonstrate the successful performance of our research infrastructure across multiple data collections.
We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time.
arXiv Detail & Related papers (2021-06-10T15:49:58Z) - Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using
Reinforcement Learning [0.0]
Reinforcement learning (RL) is an effective technique for training decision-making agents through interactions with their environment.
In digital advertising, real-time bidding (RTB) is a common method of allocating advertising inventory through real-time auctions.
arXiv Detail & Related papers (2021-05-21T21:56:12Z) - Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential
Advertising [52.3825928886714]
We formulate the sequential advertising strategy optimization as a dynamic knapsack problem.
We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space.
To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
arXiv Detail & Related papers (2020-06-29T18:50:35Z) - MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding [47.555870679348416]
We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals.
Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
arXiv Detail & Related papers (2020-02-18T07:16:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.