A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions
arXiv:2606.28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge. Existing methods often rely on rigid explore-then-exploit schedules, assume stationary adversaries, and optimize solely for bidder utility, thereby limiting adaptability and strategic robustness. To address these limitations, we introduce the A3M framework, which integrates adaptive deep reinforcement learning (DRL), explicit adversarial reasoning, and principled multi-objective reward design for online auction strategy optimization. A3M employs
The increasing complexity and speed of online markets, coupled with advancements in deep reinforcement learning, create an urgent need for more sophisticated and adaptive bidding strategies.
This research introduces a novel framework for resilient and strategically robust bidding in adversarial environments, which is crucial for maximizing utility in competitive digital economies and potentially informing general AI agent design.
Traditional, rigid bidding strategies are challenged by this adaptive, multi-objective approach, suggesting a future where AI agents can fluidly adjust to complex market dynamics and adversarial actions.
- · Companies with sophisticated AI and data science teams
- · Digital advertising platforms
- · E-commerce businesses
- · Auction-based marketplaces
- · Companies relying on static bidding algorithms
- · Less technologically advanced market participants
More efficient and dynamic allocation of resources in digital auctions, potentially increasing overall market efficiency.
Increased barrier to entry for smaller players without advanced AI capabilities, centralizing power among technologically advanced firms.
The development of 'AI arms races' in various online marketplaces, where competing AI agents constantly refine and counter each other's strategies.
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Read at arXiv cs.LG