MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Advertising Systems

The article presents MaRCA, a multi‑agent reinforcement learning framework that models user value, compute consumption, and action reward to allocate limited computation resources across the entire advertising recommendation pipeline, achieving higher ad revenue while keeping system load stable under fluctuating traffic and diverse request values.

Deep LearningLoad-Aware SchedulingResource Optimization

0 likes · 16 min read

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Advertising Systems

JD Tech Talk

Mar 24, 2025 · Artificial Intelligence

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Ad Serving

This article presents MaRCA, a multi‑agent reinforcement learning framework that allocates computation resources across the full ad‑serving chain by modeling user value, compute consumption, and action rewards, enabling fine‑grained power‑tilting toward high‑quality traffic and achieving significant business gains under strict latency constraints.

AI Optimizationad servingcomputation allocation

0 likes · 16 min read

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Ad Serving

JD Cloud Developers

Mar 24, 2025 · Artificial Intelligence

How Multi-Agent Reinforcement Learning Boosts Ad Computation Allocation

This article presents MaRCA, a multi‑agent reinforcement‑learning framework that allocates computation resources across the full ad‑serving chain, modeling user value, compute cost, and action rewards to maximize ad revenue while keeping system load stable under fluctuating traffic.

AIMulti-Agentad optimization

0 likes · 16 min read

How Multi-Agent Reinforcement Learning Boosts Ad Computation Allocation