A3M integrates adaptive DRL, adversarial opponent modeling, and multi-objective rewards to cut regret 30-40% versus baselines while remaining robust to strategy shifts in repeated auctions.
Grafana Labs
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UNVERDICTED 2representative citing papers
AOI is a multi-agent system that dynamically schedules operations and compresses context hierarchically to achieve 72% compression while preserving 93% critical information and cutting repair times by 34%.
citing papers explorer
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A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions
A3M integrates adaptive DRL, adversarial opponent modeling, and multi-objective rewards to cut regret 30-40% versus baselines while remaining robust to strategy shifts in repeated auctions.
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AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression
AOI is a multi-agent system that dynamically schedules operations and compresses context hierarchically to achieve 72% compression while preserving 93% critical information and cutting repair times by 34%.